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Dissertations / Theses on the topic 'Markov chain Monte Carlo (MCMC)'
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Guha, Subharup. "Benchmark estimation for Markov Chain Monte Carlo samplers." The Ohio State University, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=osu1085594208.
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Angelino, Elaine Lee. "Accelerating Markov chain Monte Carlo via parallel predictive prefetching." Thesis, Harvard University, 2014. http://nrs.harvard.edu/urn-3:HUL.InstRepos:13070022.
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We present a general framework for accelerating a large class of widely used Markov chain Monte Carlo (MCMC) algorithms. This dissertation demonstrates that MCMC inference can be accelerated in a model of parallel computation that uses speculation to predict and complete computational work ahead of when it is known to be useful. By exploiting fast, iterative approximations to the target density, we can speculatively evaluate many potential future steps of the chain in parallel. In Bayesian inference problems, this approach can accelerate sampling from the target distribution, without compromising exactness, by exploiting subsets of data. It takes advantage of whatever parallel resources are available, but produces results exactly equivalent to standard serial execution. In the initial burn-in phase of chain evaluation, it achieves speedup over serial evaluation that is close to linear in the number of available cores.Engineering and Applied Sciences
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Browne, William J. "Applying MCMC methods to multi-level models." Thesis, University of Bath, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.268210.
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Durmus, Alain. "High dimensional Markov chain Monte Carlo methods : theory, methods and applications." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLT001/document.
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L'objet de cette thèse est l'analyse fine de méthodes de Monte Carlopar chaînes de Markov (MCMC) et la proposition de méthodologies nouvelles pour échantillonner une mesure de probabilité en grande dimension. Nos travaux s'articulent autour de trois grands sujets.Le premier thème que nous abordons est la convergence de chaînes de Markov en distance de Wasserstein. Nous établissons des bornes explicites de convergence géométrique et sous-géométrique. Nous appliquons ensuite ces résultats à l'étude d'algorithmes MCMC. Nous nous intéressons à une variante de l'algorithme de Metropolis-Langevin ajusté (MALA) pour lequel nous donnons des bornes explicites de convergence. Le deuxième algorithme MCMC que nous analysons est l'algorithme de Crank-Nicolson pré-conditionné, pour lequel nous montrerons une convergence sous-géométrique.Le second objet de cette thèse est l'étude de l'algorithme de Langevin unajusté (ULA). Nous nous intéressons tout d'abord à des bornes explicites en variation totale suivant différentes hypothèses sur le potentiel associé à la distribution cible. Notre étude traite le cas où le pas de discrétisation est maintenu constant mais aussi du cas d'une suite de pas tendant vers 0. Nous prêtons dans cette étude une attention toute particulière à la dépendance de l'algorithme en la dimension de l'espace d'état. Dans le cas où la densité est fortement convexe, nous établissons des bornes de convergence en distance de Wasserstein. Ces bornes nous permettent ensuite de déduire des bornes de convergence en variation totale qui sont plus précises que celles reportées précédemment sous des conditions plus faibles sur le potentiel. Le dernier sujet de cette thèse est l'étude des algorithmes de type Metropolis-Hastings par échelonnage optimal. Tout d'abord, nous étendons le résultat pionnier sur l'échelonnage optimal de l'algorithme de Metropolis à marche aléatoire aux densités cibles dérivables en moyenne Lp pour p ≥ 2. Ensuite, nous proposons de nouveaux algorithmes de type Metropolis-Hastings qui présentent un échelonnage optimal plus avantageux que celui de l'algorithme MALA. Enfin, nous analysons la stabilité et la convergence en variation totale de ces nouveaux algorithmesThe subject of this thesis is the analysis of Markov Chain Monte Carlo (MCMC) methods and the development of new methodologies to sample from a high dimensional distribution. Our work is divided into three main topics. The first problem addressed in this manuscript is the convergence of Markov chains in Wasserstein distance. Geometric and sub-geometric convergence with explicit constants, are derived under appropriate conditions. These results are then applied to thestudy of MCMC algorithms. The first analyzed algorithm is an alternative scheme to the Metropolis Adjusted Langevin algorithm for which explicit geometric convergence bounds are established. The second method is the pre-Conditioned Crank-Nicolson algorithm. It is shown that under mild assumption, the Markov chain associated with thisalgorithm is sub-geometrically ergodic in an appropriated Wasserstein distance. The second topic of this thesis is the study of the Unadjusted Langevin algorithm (ULA). We are first interested in explicit convergence bounds in total variation under different kinds of assumption on the potential associated with the target distribution. In particular, we pay attention to the dependence of the algorithm on the dimension of the state space. The case of fixed step sizes as well as the case of nonincreasing sequences of step sizes are dealt with. When the target density is strongly log-concave, explicit bounds in Wasserstein distance are established. These results are then used to derived new bounds in the total variation distance which improve the one previously derived under weaker conditions on the target density.The last part tackles new optimal scaling results for Metropolis-Hastings type algorithms. First, we extend the pioneer result on the optimal scaling of the random walk Metropolis algorithm to target densities which are differentiable in Lp mean for p ≥ 2. Then, we derive new Metropolis-Hastings type algorithms which have a better optimal scaling compared the MALA algorithm. Finally, the stability and the convergence in total variation of these new algorithms are studied
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Harkness, Miles Adam. "Parallel simulation, delayed rejection and reversible jump MCMC for object recognition." Thesis, University of Bristol, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.324266.
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Smith, Corey James. "Exact Markov Chain Monte Carlo with Likelihood Approximations for Functional Linear Models." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531833318013379.
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Walker, Neil Rawlinson. "A Bayesian approach to the job search model and its application to unemployment durations using MCMC methods." Thesis, University of Newcastle Upon Tyne, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299053.
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Jeon, Juncheol. "Deterioration model for ports in the Republic of Korea using Markov chain Monte Carlo with multiple imputation." Thesis, University of Dundee, 2019. https://discovery.dundee.ac.uk/en/studentTheses/1cc538ea-1468-4d51-bcf8-711f8b9912f9.
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Condition of infrastructure is deteriorated over time as it gets older. It is the deterioration model that predicts how and when facilities will deteriorate over time. In most infrastructure management system, the deterioration model is a crucial element. Using the deterioration model, it is very helpful to estimate when repair will be carried out, how much will be needed for the maintenance of the entire facilities, and what maintenance costs will be required during the life cycle of the facility. However, the study of deterioration model for civil infrastructures of ports is still in its infancy. In particular, there is almost no related research in South Korea. Thus, this study aims to develop a deterioration model for civil infrastructure of ports in South Korea. There are various methods such as Deterministic, Stochastic, and Artificial Intelligence to develop deterioration model. In this research, Markov model using Markov chain theory, one of the Stochastic methods, is used to develop deterioration model for ports in South Korea. Markov chain is a probabilistic process among states. i.e., in Markov chain, transition among states follows some probability which is called as the transition probability. The key process of developing Markov model is to find this transition probability. This process is called calibration. In this study, the existing methods, Optimization method and Markov Chain Monte Carlo (MCMC), are reviewed, and methods to improve for these are presented. In addition, in this study, only a small amount of data are used, which causes distortion of the model. Thus, supplement techniques are presented to overcome the small size of data. In order to address the problem of the existing methods and the lack of data, the deterioration model developed by the four calibration methods: Optimization, Optimization with Bootstrap, MCMC (Markov Chain Monte Carlo), and MCMC with Multiple imputation, are finally proposed in this study. In addition, comparison between four models are carried out and good performance model is proposed. This research provides deterioration model for port in South Korea, and more accurate calibration technique is suggested. Furthermore, the method of supplementing insufficient data has been combined with existing calibration techniques.
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Fu, Jianlin. "A markov chain monte carlo method for inverse stochastic modeling and uncertainty assessment." Doctoral thesis, Universitat Politècnica de València, 2008. http://hdl.handle.net/10251/1969.
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Unlike the traditional two-stage methods, a conditional and inverse-conditional simulation approach may directly generate independent, identically distributed realizations to honor both static data and state data in one step. The Markov chain Monte Carlo (McMC) method was proved a powerful tool to perform such type of stochastic simulation. One of the main advantages of the McMC over the traditional sensitivity-based optimization methods to inverse problems is its power, flexibility and well-posedness in incorporating observation data from different sources. In this work, an improved version of the McMC method is presented to perform the stochastic simulation of reservoirs and aquifers in the framework of multi-Gaussian geostatistics.
First, a blocking scheme is proposed to overcome the limitations of the classic single-component Metropolis-Hastings-type McMC. One of the main characteristics of the blocking McMC (BMcMC) scheme is that, depending on the inconsistence between the prior model and the reality, it can preserve the prior spatial structure and statistics as users specified. At the same time, it improves the mixing of the Markov chain and hence enhances the computational efficiency of the McMC. Furthermore, the exploration ability and the mixing speed of McMC are efficiently improved by coupling the multiscale proposals, i.e., the coupled multiscale McMC method. In order to make the BMcMC method capable of dealing with the high-dimensional cases, a multi-scale scheme is introduced to accelerate the computation of the likelihood which greatly improves the computational efficiency of the McMC due to the fact that most of the computational efforts are spent on the forward simulations. To this end, a flexible-grid full-tensor finite-difference simulator, which is widely compatible with the outputs from various upscaling subroutines, is developed to solve the flow equations and a constant-displacement random-walk
particle-tracking method, which enhances the comFu, J. (2008). A markov chain monte carlo method for inverse stochastic modeling and uncertainty assessment [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/1969
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Lindahl, John, and Douglas Persson. "Data-driven test case design of automatic test cases using Markov chains and a Markov chain Monte Carlo method." Thesis, Malmö universitet, Fakulteten för teknik och samhälle (TS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-43498.
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Large and complex software that is frequently changed leads to testing challenges. It is well established that the later a fault is detected in software development, the more it costs to fix. This thesis aims to research and develop a method of generating relevant and non-redundant test cases for a regression test suite, to catch bugs as early in the development process as possible. The research was executed at Axis Communications AB with their products and systems in mind. The approach utilizes user data to dynamically generate a Markov chain model and with a Markov chain Monte Carlo method, strengthen that model. The model generates test case proposals, detects test gaps, and identifies redundant test cases based on the user data and data from a test suite. The sampling in the Markov chain Monte Carlo method can be modified to bias the model for test coverage or relevancy. The model is generated generically and can therefore be implemented in other API-driven systems. The model was designed with scalability in mind and further implementations can be made to increase the complexity and further specialize the model for individual needs.
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Lindsten, Fredrik. "Particle filters and Markov chains for learning of dynamical systems." Doctoral thesis, Linköpings universitet, Reglerteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-97692.
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Sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) methods provide computational tools for systematic inference and learning in complex dynamical systems, such as nonlinear and non-Gaussian state-space models. This thesis builds upon several methodological advances within these classes of Monte Carlo methods.Particular emphasis is placed on the combination of SMC and MCMC in so called particle MCMC algorithms. These algorithms rely on SMC for generating samples from the often highly autocorrelated state-trajectory. A specific particle MCMC algorithm, referred to as particle Gibbs with ancestor sampling (PGAS), is suggested. By making use of backward sampling ideas, albeit implemented in a forward-only fashion, PGAS enjoys good mixing even when using seemingly few particles in the underlying SMC sampler. This results in a computationally competitive particle MCMC algorithm. As illustrated in this thesis, PGAS is a useful tool for both Bayesian and frequentistic parameter inference as well as for state smoothing. The PGAS sampler is successfully applied to the classical problem of Wiener system identification, and it is also used for inference in the challenging class of non-Markovian latent variable models.Many nonlinear models encountered in practice contain some tractable substructure. As a second problem considered in this thesis, we develop Monte Carlo methods capable of exploiting such substructures to obtain more accurate estimators than what is provided otherwise. For the filtering problem, this can be done by using the well known Rao-Blackwellized particle filter (RBPF). The RBPF is analysed in terms of asymptotic variance, resulting in an expression for the performance gain offered by Rao-Blackwellization. Furthermore, a Rao-Blackwellized particle smoother is derived, capable of addressing the smoothing problem in so called mixed linear/nonlinear state-space models. The idea of Rao-Blackwellization is also used to develop an online algorithm for Bayesian parameter inference in nonlinear state-space models with affine parameter dependencies.CNDM
CADICS
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Byng, Martyn Charles. "A statistical model for locating regulatory regions in novel DNA sequences." Thesis, University of Reading, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.369119.
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Frühwirth-Schnatter, Sylvia. "MCMC Estimation of Classical and Dynamic Switching and Mixture Models." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1998. http://epub.wu.ac.at/698/1/document.pdf.
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In the present paper we discuss Bayesian estimation of a very general model class where the distribution of the observations is assumed to depend on a latent mixture or switching variable taking values in a discrete state space. This model class covers e.g. finite mixture modelling, Markov switching autoregressive modelling and dynamic linear models with switching. Joint Bayesian estimation of all latent variables, model parameters and parameters determining the probability law of the switching variable is carried out by a new Markov Chain Monte Carlo method called permutation sampling. Estimation of switching and mixture models is known to be faced with identifiability problems as switching and mixture are identifiable only up to permutations of the indices of the states. For a Bayesian analysis the posterior has to be constrained in such a way that identifiablity constraints are fulfilled. The permutation sampler is designed to sample efficiently from the constrained posterior, by first sampling from the unconstrained posterior - which often can be done in a convenient multimove manner - and then by applying a suitable permutation, if the identifiability constraint is violated. We present simple conditions on the prior which ensure that this method is a valid Markov Chain Monte Carlo method (that is invariance, irreducibility and aperiodicity hold). Three case studies are presented, including finite mixture modelling of fetal lamb data, Markov switching Autoregressive modelling of the U.S. quarterly real GDP data, and modelling the U .S./U.K. real exchange rate by a dynamic linear model with Markov switching heteroscedasticity. (author's abstract)Series: Forschungsberichte / Institut für Statistik
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Karagiannis, Georgios. "AISRJMCMC - Annealed Importance Sampling within Reversible Jump Markov Chain Monte Carlo algorithm : a pseudo-marginal reversible jump MCMC algorithm." Thesis, University of Bristol, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.546223.
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Walker, Matthew James. "Methods for Bayesian inversion of seismic data." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/10504.
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The purpose of Bayesian seismic inversion is to combine information derived from seismic data and prior geological knowledge to determine a posterior probability distribution over parameters describing the elastic and geological properties of the subsurface. Typically the subsurface is modelled by a cellular grid model containing thousands or millions of cells within which these parameters are to be determined. Thus such inversions are computationally expensive due to the size of the parameter space (being proportional to the number of grid cells) over which the posterior is to be determined. Therefore, in practice approximations to Bayesian seismic inversion must be considered. A particular, existing approximate workflow is described in this thesis: the so-called two-stage inversion method explicitly splits the inversion problem into elastic and geological inversion stages. These two stages sequentially estimate the elastic parameters given the seismic data, and then the geological parameters given the elastic parameter estimates, respectively. In this thesis a number of methodologies are developed which enhance the accuracy of this approximate workflow. To reduce computational cost, existing elastic inversion methods often incorporate only simplified prior information about the elastic parameters. Thus a method is introduced which transforms such results, obtained using prior information specified using only two-point geostatistics, into new estimates containing sophisticated multi-point geostatistical prior information. The method uses a so-called deep neural network, trained using only synthetic instances (or `examples') of these two estimates, to apply this transformation. The method is shown to improve the resolution and accuracy (by comparison to well measurements) of elastic parameter estimates determined for a real hydrocarbon reservoir. It has been shown previously that so-called mixture density network (MDN) inversion can be used to solve geological inversion analytically (and thus very rapidly and efficiently) but only under certain assumptions about the geological prior distribution. A so-called prior replacement operation is developed here, which can be used to relax these requirements. It permits the efficient MDN method to be incorporated into general stochastic geological inversion methods which are free from the restrictive assumptions. Such methods rely on the use of Markov-chain Monte-Carlo (MCMC) sampling, which estimate the posterior (over the geological parameters) by producing a correlated chain of samples from it. It is shown that this approach can yield biased estimates of the posterior. Thus an alternative method which obtains a set of non-correlated samples from the posterior is developed, avoiding the possibility of bias in the estimate. The new method was tested on a synthetic geological inversion problem; its results compared favourably to those of Gibbs sampling (a MCMC method) on the same problem, which exhibited very significant bias. The geological prior information used in seismic inversion can be derived from real images which bear similarity to the geology anticipated within the target region of the subsurface. Such so-called training images are not always available from which this information (in the form of geostatistics) may be extracted. In this case appropriate training images may be generated by geological experts. However, this process can be costly and difficult. Thus an elicitation method (based on a genetic algorithm) is developed here which obtains the appropriate geostatistics reliably and directly from a geological expert, without the need for training images. 12 experts were asked to use the algorithm (individually) to determine the appropriate geostatistics for a physical (target) geological image. The majority of the experts were able to obtain a set of geostatistics which were consistent with the true (measured) statistics of the target image.
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Cauchemez, Simon. "Estimation des paramètres de transmission dans les modèles épidémiques par échantillonnage de Monte Carlo par chaine de Markov." Paris 6, 2005. http://www.theses.fr/2005PA066572.
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Loza, Reyes Elisa. "Classification of phylogenetic data via Bayesian mixture modelling." Thesis, University of Bath, 2010. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.519916.
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Conventional probabilistic models for phylogenetic inference assume that an evolutionary tree,andasinglesetofbranchlengthsandstochasticprocessofDNA evolutionare sufficient to characterise the generating process across an entire DNA alignment. Unfortunately such a simplistic, homogeneous formulation may be a poor description of reality when the data arise from heterogeneous processes. A well-known example is when sites evolve at heterogeneous rates. This thesis is a contribution to the modelling and understanding of heterogeneityin phylogenetic data. Weproposea methodfor the classificationof DNA sites based on Bayesian mixture modelling. Our method not only accounts for heterogeneous data but also identifies the underlying classes and enables their interpretation. We also introduce novel MCMC methodology with the same, or greater, estimation performance than existing algorithms but with lower computational cost. We find that our mixture model can successfully detect evolutionary heterogeneity and demonstrate its direct relevance by applying it to real DNA data. One of these applications is the analysis of sixteen strains of one of the bacterial species that cause Lyme disease. Results from that analysis have helped understanding the evolutionary paths of these bacterial strains and, therefore, the dynamics of the spread of Lyme disease. Our method is discussed in the context of DNA but it may be extendedto othertypesof molecular data. Moreover,the classification scheme thatwe propose is evidence of the breadth of application of mixture modelling and a step forwards in the search for more realistic models of theprocesses that underlie phylogenetic data.
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Garzon, Rozo Betty Johanna. "Modelling operational risk using skew t-copulas and Bayesian inference." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/25751.
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Operational risk losses are heavy tailed and are likely to be asymmetric and extremely dependent among business lines/event types. The analysis of dependence via copula models has been focussed on the bivariate case mainly. In the vast majority of instances symmetric elliptical copulas are employed to model dependence for severities. This thesis proposes a new methodology to assess, in a multivariate way, the asymmetry and extreme dependence between severities, and to calculate the capital for operational risk. This methodology simultaneously uses (i) several parametric distributions and an alternative mixture distribution (the Lognormal for the body of losses and the generalised Pareto Distribution for the tail) using a technique from extreme value theory, (ii) the multivariate skew t-copula applied for the first time across severities and (iii) Bayesian theory. The former to model severities, I test simultaneously several parametric distributions and the mixture distribution for each business line. This procedure enables me to achieve multiple combinations of the severity distribution and to find which fits most closely. The second to effectively model asymmetry and extreme dependence in high dimensions. The third to estimate the copula model, given the high multivariate component (i.e. eight business lines and seven event types) and the incorporation of mixture distributions it is highly difficult to implement maximum likelihood. Therefore, I use a Bayesian inference framework and Markov chain Monte Carlo simulation to evaluate the posterior distribution to estimate and make inferences of the parameters of the skew t-copula model. The research analyses an updated operational loss data set, SAS® Operational Risk Global Data (SAS OpRisk Global Data), to model operational risk at international financial institutions. I then evaluate the impact of this multivariate, asymmetric and extreme dependence on estimating the total regulatory capital, among other established multivariate copulas. My empirical findings are consistent with other studies reporting thin and medium-tailed loss distributions. My approach substantially outperforms symmetric elliptical copulas, demonstrating that modelling dependence via the skew t-copula provides a more efficient allocation of capital charges of up to 56% smaller than that indicated by the standard Basel model.
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Miazhynskaia, Tatiana, Sylvia Frühwirth-Schnatter, and Georg Dorffner. "A comparison of Bayesian model selection based on MCMC with an application to GARCH-type models." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2003. http://epub.wu.ac.at/586/1/document.pdf.
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This paper presents a comprehensive review and comparison of five computational methods for Bayesian model selection, based on MCMC simulations from posterior model parameter distributions. We apply these methods to a well-known and important class of models in financial time series analysis, namely GARCH and GARCH-t models for conditional return distributions (assuming normal and t-distributions). We compare their performance vis--vis the more common maximum likelihood-based model selection on both simulated and real market data. All five MCMC methods proved feasible in both cases, although differing in their computational demands. Results on simulated data show that for large degrees of freedom (where the t-distribution becomes more similar to a normal one), Bayesian model selection results in better decisions in favour of the true model than maximum likelihood. Results on market data show the feasibility of all model selection methods, mainly because the distributions appear to be decisively non-Gaussian.Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Delescluse, Matthieu. "Une approche Monte Carlo par chaînes de Markov pour la classification des potentiels d' action : application à l' étude des corrélations d' activité des cellules de Purkinje." Paris 6, 2005. http://www.theses.fr/2005PA066493.
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Quiroz, Matias. "Bayesian Inference in Large Data Problems." Doctoral thesis, Stockholms universitet, Statistiska institutionen, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-118836.
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In the last decade or so, there has been a dramatic increase in storage facilities and the possibility of processing huge amounts of data. This has made large high-quality data sets widely accessible for practitioners. This technology innovation seriously challenges traditional modeling and inference methodology. This thesis is devoted to developing inference and modeling tools to handle large data sets. Four included papers treat various important aspects of this topic, with a special emphasis on Bayesian inference by scalable Markov Chain Monte Carlo (MCMC) methods. In the first paper, we propose a novel mixture-of-experts model for longitudinal data. The model and inference methodology allows for manageable computations with a large number of subjects. The model dramatically improves the out-of-sample predictive density forecasts compared to existing models. The second paper aims at developing a scalable MCMC algorithm. Ideas from the survey sampling literature are used to estimate the likelihood on a random subset of data. The likelihood estimate is used within the pseudomarginal MCMC framework and we develop a theoretical framework for such algorithms based on subsets of the data. The third paper further develops the ideas introduced in the second paper. We introduce the difference estimator in this framework and modify the methods for estimating the likelihood on a random subset of data. This results in scalable inference for a wider class of models. Finally, the fourth paper brings the survey sampling tools for estimating the likelihood developed in the thesis into the delayed acceptance MCMC framework. We compare to an existing approach in the literature and document promising results for our algorithm.At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: Submitted. Paper 2: Submitted. Paper 3: Manuscript. Paper 4: Manuscript.
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Wang, Xiao. "Computational Modeling for Differential Analysis of RNA-seq and Methylation data." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/72271.
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Computational systems biology is an inter-disciplinary field that aims to develop computational approaches for a system-level understanding of biological systems. Advances in high-throughput biotechnology offer broad scope and high resolution in multiple disciplines. However, it is still a major challenge to extract biologically meaningful information from the overwhelming amount of data generated from biological systems. Effective computational approaches are of pressing need to reveal the functional components. Thus, in this dissertation work, we aim to develop computational approaches for differential analysis of RNA-seq and methylation data to detect aberrant events associated with cancers.
We develop a novel Bayesian approach, BayesIso, to identify differentially expressed isoforms from RNA-seq data. BayesIso features a joint model of the variability of RNA-seq data and the differential state of isoforms. BayesIso can not only account for the variability of RNA-seq data but also combines the differential states of isoforms as hidden variables for differential analysis. The differential states of isoforms are estimated jointly with other model parameters through a sampling process, providing an improved performance in detecting isoforms of less differentially expressed.
We propose to develop a novel probabilistic approach, DM-BLD, in a Bayesian framework to identify differentially methylated genes. The DM-BLD approach features a hierarchical model, built upon Markov random field models, to capture both the local dependency of measured loci and the dependency of methylation change. A Gibbs sampling procedure is designed to estimate the posterior distribution of the methylation change of CpG sites. Then, the differential methylation score of a gene is calculated from the estimated methylation changes of the involved CpG sites and the significance of genes is assessed by permutation-based statistical tests.
We have demonstrated the advantage of the proposed Bayesian approaches over conventional methods for differential analysis of RNA-seq data and methylation data. The joint estimation of the posterior distributions of the variables and model parameters using sampling procedure has demonstrated the advantage in detecting isoforms or methylated genes of less differential. The applications to breast cancer data shed light on understanding the molecular mechanisms underlying breast cancer recurrence, aiming to identify new molecular targets for breast cancer treatment.Ph. D.
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Alasseur, Clémence. "Signaux à propriétés markoviennes sous-jacentes et leur utilisation pour la modélisation de l'atténuation dans les transmissions satellites mobiles en bandes Ku et Ka." Paris 11, 2005. http://www.theses.fr/2005PA112216.
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La demande croissante de services satellites en bande large ainsi que la congestion des systèmes opérant aux fréquences traditionnelles poussent à envisager de nouveaux systèmes à des bandes de transmission supérieures à 10 GHz. Mais les comportements du LMSC (Land Mobile Satellite Channel) sont alors mal connus et nécessitent de tenir compte des perturbations atmosphériques, notamment la pluie. La mobilité des antennes pour l'émission et la réception fait également partie des problèmes des services satellites. Pour concevoir de nouveaux systèmes et des méthodes de compensation des pertes par adaptation dynamique, des modèles du canal et des précipitations sont alors nécessaires. Notre travail apporte tout d'abord une analysis du canal de propagation satellite aux fréquences Ku et Ka par l'étude d'une part de la puissance normalisée reçue et d'autre part des séries temporelles de taux de précipitation. Nous proposons ensuite des modèles ainsi que des méthodes d'extraction de leurs paramètres pour ces deux types de signaux. Deux approches, basées sur des outils MCMC (Monte Carlo Markov Chain), permettent une segmentation de la puissance normalisée du canal ainsi que l'extraction des paramètres du modèle de Markov caché sous-jacent. Une procédure d'évaluation d'une chaîne de Markov à deux niveaux pour modéliser le signal de taux de précipitation est également décrite. Enfin les méthodes développées sont appliquées à des données expérimentales et fournissent des modèles markoviens du signal de puissance du canal satellite et des taux de précipitation. La comparaison des statistiques du premier et second ordre entre les modèles et les mesures atteste de leur qualitéThe increased demand in wide band satellite applications and the congestion of services to traditional frequency bands lead to the consideration of new systems. At frequencies higher than 10GHz, the behaviours of the LMSC (Land Mobile Satellite Channel) are not well known and the influence of atmospheric perturbations, like rain, must be taken into account. For future satellite services, another matter is the potential mobility of the receiving and emitting antennas. Nevertheless, the conception of new systems and of adaptive methods to compensate losses requires models. First, our work provides an analysis of the transmission satellite channel at Ku and Ka frequency bands with the study of the normalised transmission power and of rain rate time series. We then propose approaches to extract models for these two kinds of signals. Two methods, which resort to MCMC (Monte Carlo Markov Chain) tools, permit to segment the normalised power of the channel and to extract the parameters of the underlying hidden Markov model. We also describe a way to evaluate a two level Markov model for the rain rate signal. Finally, the developed methods are applied to experimental data. They produce Markov model for the normalised power of the satellite channel and for the rain rate. Their quality is assessed by the comparison of the first and second order statistics of the models and the experimental measures
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Krometis, Justin. "A Bayesian Approach to Estimating Background Flows from a Passive Scalar." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/83783.
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We consider the statistical inverse problem of estimating a background flow field (e.g., of air or water) from the partial and noisy observation of a passive scalar (e.g., the concentration of a pollutant). Here the unknown is a vector field that is specified by large or infinite number of degrees of freedom. We show that the inverse problem is ill-posed, i.e., there may be many or no background flows that match a given set of observations. We therefore adopt a Bayesian approach, incorporating prior knowledge of background flows and models of the observation error to develop probabilistic estimates of the fluid flow. In doing so, we leverage frameworks developed in recent years for infinite-dimensional Bayesian inference. We provide conditions under which the inference is consistent, i.e., the posterior measure converges to a Dirac measure on the true background flow as the number of observations of the solute concentration grows large. We also define several computationally-efficient algorithms adapted to the problem. One is an adjoint method for computation of the gradient of the log likelihood, a key ingredient in many numerical methods. A second is a particle method that allows direct computation of point observations of the solute concentration, leveraging the structure of the inverse problem to avoid approximation of the full infinite-dimensional scalar field. Finally, we identify two interesting example problems with very different posterior structures, which we use to conduct a large-scale benchmark of the convergence of several Markov Chain Monte Carlo methods that have been developed in recent years for infinite-dimensional settings.Ph. D.
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Deng, Wei. "Multiple imputation for marginal and mixed models in longitudinal data with informative missingness." Connect to resource, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1126890027.
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Thesis (Ph. D.)--Ohio State University, 2005.Title from first page of PDF file. Document formatted into pages; contains xiii, 108 p.; also includes graphics. Includes bibliographical references (p. 104-108). Available online via OhioLINK's ETD Center
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Shi, Xu. "Bayesian Modeling for Isoform Identification and Phenotype-specific Transcript Assembly." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/79772.
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The rapid development of biotechnology has enabled researchers to collect high-throughput data for studying various biological processes at the genomic level, transcriptomic level, and proteomic level. Due to the large noise in the data and the high complexity of diseases (such as cancer), it is a challenging task for researchers to extract biologically meaningful information that can help reveal the underlying molecular mechanisms. The challenges call for more efforts in developing efficient and effective computational methods to analyze the data at different levels so as to understand the biological systems in different aspects. In this dissertation research, we have developed novel Bayesian approaches to infer alternative splicing mechanisms in biological systems using RNA sequencing data.
Specifically, we focus on two research topics in this dissertation: isoform identification and phenotype-specific transcript assembly. For isoform identification, we develop a computational approach, SparseIso, to jointly model the existence and abundance of isoforms in a Bayesian framework. A spike-and-slab prior is incorporated into the model to enforce the sparsity of expressed isoforms. A Gibbs sampler is developed to sample the existence and abundance of isoforms iteratively. For transcript assembly, we develop a Bayesian approach, IntAPT, to assemble phenotype-specific transcripts from multiple RNA sequencing profiles. A two-layer Bayesian framework is used to model the existence of phenotype-specific transcripts and the transcript abundance in individual samples. Based on the hierarchical Bayesian model, a Gibbs sampling algorithm is developed to estimate the joint posterior distribution for phenotype-specific transcript assembly. The performances of our proposed methods are
evaluated with simulation data, compared with existing methods and benchmarked with real cell line data. We then apply our methods on breast cancer data to identify biologically meaningful splicing mechanisms associated with breast cancer. For the further work, we will extend our methods for de novo transcript assembly to identify novel isoforms in biological systems; we will incorporate isoform-specific networks into our methods to better understand splicing mechanisms in biological systems.Ph. D.
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Kastner, Gregor, and Sylvia Frühwirth-Schnatter. "Ancillarity-Sufficiency Interweaving Strategy (ASIS) for Boosting MCMC Estimation of Stochastic Volatility Models." WU Vienna University of Economics and Business, 2013. http://epub.wu.ac.at/3771/1/paper.pdf.
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Bayesian inference for stochastic volatility models using MCMC methods highly depends
on actual parameter values in terms of sampling efficiency. While draws from the posterior
utilizing the standard centered parameterization break down when the volatility of volatility parameter
in the latent state equation is small, non-centered versions of the model show deficiencies
for highly persistent latent variable series. The novel approach of ancillarity-sufficiency
interweaving has recently been shown to aid in overcoming these issues for a broad class of
multilevel models. In this paper, we demonstrate how such an interweaving strategy can be
applied to stochastic volatility models in order to greatly improve sampling efficiency for all
parameters and throughout the entire parameter range. Moreover, this method of "combining
best of different worlds" allows for inference for parameter constellations that have previously
been infeasible to estimate without the need to select a particular parameterization beforehand.Series: Research Report Series / Department of Statistics and Mathematics
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Tsai, Tsung-Heng. "Bayesian Alignment Model for Analysis of LC-MS-based Omic Data." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/64151.
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Liquid chromatography coupled with mass spectrometry (LC-MS) has been widely used in various omic studies for biomarker discovery. Appropriate LC-MS data preprocessing steps are needed to detect true differences between biological groups. Retention time alignment is one of the most important yet challenging preprocessing steps, in order to ensure that ion intensity measurements among multiple LC-MS runs are comparable. In this dissertation, we propose a Bayesian alignment model (BAM) for analysis of LC-MS data. BAM uses Markov chain Monte Carlo (MCMC) methods to draw inference on the model parameters and provides estimates of the retention time variability along with uncertainty measures, enabling a natural framework to integrate information of various sources. From methodology development to practical application, we investigate the alignment problem through three research topics: 1) development of single-profile Bayesian alignment model, 2) development of multi-profile Bayesian alignment model, and 3) application to biomarker discovery research.
Chapter 2 introduces the profile-based Bayesian alignment using a single chromatogram, e.g., base peak chromatogram from each LC-MS run. The single-profile alignment model improves on existing MCMC-based alignment methods through 1) the implementation of an efficient MCMC sampler using a block Metropolis-Hastings algorithm, and 2) an adaptive mechanism for knot specification using stochastic search variable selection (SSVS).
Chapter 3 extends the model to integrate complementary information that better captures the variability in chromatographic separation. We use Gaussian process regression on the internal standards to derive a prior distribution for the mapping functions. In addition, a clustering approach is proposed to identify multiple representative chromatograms for each LC-MS run. With the Gaussian process prior, these chromatograms are simultaneously considered in the profile-based alignment, which greatly improves the model estimation and facilitates the subsequent peak matching process.
Chapter 4 demonstrates the applicability of the proposed Bayesian alignment model to biomarker discovery research. We integrate the proposed Bayesian alignment model into a rigorous preprocessing pipeline for LC-MS data analysis. Through the developed analysis pipeline, candidate biomarkers for hepatocellular carcinoma (HCC) are identified and confirmed on a complementary platform.Ph. D.
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SILVA, José Rodrigo Santos. "Otimização do método área-velocidade para estimação de vazão fluvial usando MCMC." Universidade Federal Rural de Pernambuco, 2011. http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/5010.
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The velocity-area method is a standard procedure for measurement of river discharge, with wide application in hydrometric studies, standardized at the international level by the norm ISO 748:2007 of the International Standards Organization. This method requires measurement of velocity at several verticals of the river, at different depths for each vertical. In general, a relatively high number of measurements is necessary do determine the discharge. Recently a technique was proposed which results in a robust estimate of river discharge using a reduced number of measurement points, based on elementary properties of fluid dynamics, stemming from the Navier-Stokes equations, and the use of continuous interpolation between the verticals for calculating velocity across the entire river cross section. In the present work the Monte Carlo Markov Chain (MCMC) method is used to search for the optimum positions for velocity measurement, with the objective of reducing the number of measurement points without significant loss of precision, and therefore maximizing the efficiency of the estimate. A dedicated computer algorithm was developed in C programming language and applied to measurements collected on the river Exu, state of Pernambuco, Brazil, in April 2008. It is found that the discharge estimates with three or more measurement points exhibit variations well within uncertainty limits corresponding to the full 27 point estimate using the traditional velocity-area method. Simulation results indicate that the best positions for velocity measurement are close to the surface, and that significant savings in cost and labor may be accomplished by positioning the measurements at strategic points, without precision loss.
O método área-velocidade é um procedimento utilizado para medir a descarga de rios. Esta é uma técnica bastante difundida na hidrometria, e é normatizada internacionalmente pela ISO 748:2007 da International Standard Organization. Este método requer a medição da velocidade em diversas verticais do rio, e em diferentes profundidades de cada vertical. Em geral é necessário um número relativamente elevado de medições para determinar a vazão. Recentemente foi proposta uma técnica que resulta em uma estimativa robusta da descarga fluvial com reduzido número de pontos de medida, que se baseia nas propriedades básicas da dinâmica de fluidos e nas equações de Navier- Stokes, além de utilizar uma interpolação continua para o cálculo das velocidades em toda a seção vertical. No presente trabalho, o método Monte Carlo Markov Chain (MCMC) é utilizado na busca da melhor posição das medidas de velocidade a serem realizados na seção vertical do rio, tal que seja possível reduzir o número de medições e maximizar a eficiência da estimativa. O algoritmo foi desenvolvido em linguagem C e aplicado em medidas de velocidade colhidas no riacho Exu, no estado de Pernanbuco, em abril de 2008. Estimativas de vazão realizadas a partir de 3 medidas de velocidade sobre a seção vertical mostraram-se eficientes, apresentando diferenças da estimativa obtida com 27 pontos através do método área-velocidade tradicional dentro de limites de incerteza. Os resultados de simulação indicam que os melhores locais de medição da velocidade sob a seção vertical situam-se perto da superfície do rio, e que uma economia significativa no custo e no trabalho pode ser conseguida através posicionamento dos pontos de medição em locais estratégicos, sem perda da precisão da estimativa.
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Inhasz, Juliana. "Term structure dynamics and no-arbitrage under the Taylor Rule." reponame:Repositório Institucional do FGV, 2009. http://hdl.handle.net/10438/4253.
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Previous issue date: 2009-08-18T00:00:00ZThe term structure interest rate determination is one of the main subjects of the financial assets management. Considering the great importance of the financial assets for the economic policies conduction it is basic to understand structure is determined. The main purpose of this study is to estimate the term structure of Brazilian interest rates together with short term interest rate. The term structure will be modeled based on a model with an affine structure. The estimation was made considering the inclusion of three latent factors and two macroeconomic variables, through the Bayesian technique of the Monte Carlo Markov Chain (MCMC).
A determinação da taxa de juros estrutura a termo é um dos temas principais da gestão de ativos financeiros. Considerando a grande importância dos ativos financeiros para a condução das políticas econômicas, é fundamental para compreender a estrutura que é determinado. O principal objetivo deste estudo é estimar a estrutura a termo das taxas de juros brasileiras, juntamente com taxa de juros de curto prazo. A estrutura a termo será modelado com base em um modelo com uma estrutura afim. A estimativa foi feita considerando a inclusão de três fatores latentes e duas variáveis macroeconômicas, através da técnica Bayesiana da Cadeia de Monte Carlo Markov (MCMC).
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Thouvenin, Pierre-Antoine. "Modeling spatial and temporal variabilities in hyperspectral image unmixing." Phd thesis, Toulouse, INPT, 2017. http://oatao.univ-toulouse.fr/19258/1/THOUVENIN_PierreAntoine.pdf.
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Acquired in hundreds of contiguous spectral bands, hyperspectral (HS) images have received an increasing interest due to the significant spectral information they convey about the materials present in a given scene. However, the limited spatial resolution of hyperspectral sensors implies that the observations are mixtures of multiple signatures corresponding to distinct materials. Hyperspectral unmixing is aimed at identifying the reference spectral signatures composing the data -- referred to as endmembers -- and their relative proportion in each pixel according to a predefined mixture model. In this context, a given material is commonly assumed to be represented by a single spectral signature. This assumption shows a first limitation, since endmembers may vary locally within a single image, or from an image to another due to varying acquisition conditions, such as declivity and possibly complex interactions between the incident light and the observed materials. Unless properly accounted for, spectral variability can have a significant impact on the shape and the amplitude of the acquired signatures, thus inducing possibly significant estimation errors during the unmixing process. A second limitation results from the significant size of HS data, which may preclude the use of batch estimation procedures commonly used in the literature, i.e., techniques exploiting all the available data at once. Such computational considerations notably become prominent to characterize endmember variability in multi-temporal HS (MTHS) images, i.e., sequences of HS images acquired over the same area at different time instants. The main objective of this thesis consists in introducing new models and unmixing procedures to account for spatial and temporal endmember variability. Endmember variability is addressed by considering an explicit variability model reminiscent of the total least squares problem, and later extended to account for time-varying signatures. The variability is first estimated using an unsupervised deterministic optimization procedure based on the Alternating Direction Method of Multipliers (ADMM). Given the sensitivity of this approach to abrupt spectral variations, a robust model formulated within a Bayesian framework is introduced. This formulation enables smooth spectral variations to be described in terms of spectral variability, and abrupt changes in terms of outliers. Finally, the computational restrictions induced by the size of the data is tackled by an online estimation algorithm. This work further investigates an asynchronous distributed estimation procedure to estimate the parameters of the proposed models.
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Schwarzenegger, Rafael. "Matematické modely spolehlivosti v technické praxi." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2017. http://www.nusl.cz/ntk/nusl-318802.
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Tato práce popisuje a aplikuje parametrické a neparametrické modely spolehlivosti na cenzorovaná data. Ukazuje implementaci spolehlivosti v metodologii Six Sigma. Metody jsou využity pro přežití/spolehlivost reálných technických dat.
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Milcher, Susanne, and Manfred M. Fischer. "On labour market discrimination against Roma in South East Europe." WU Vienna University of Economics and Business, 2010. http://epub.wu.ac.at/3960/1/SSRN%2Did1739103.pdf.
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This paper directs interest on country-specific labour market discrimination Roma
may suffer in South East Europe. The study lies in the tradition of statistical Blinder-Oaxaca
decomposition analysis. We use microdata from UNDP's 2004 survey of Roma minorities,
and apply a Bayesian approach, proposed by Keith and LeSage (2004), for the decomposition
analysis of wage differentials. This approach is based on a robust Bayesian heteroscedastic
linear regression model in conjunction with Markov Chain Monte Carlo (MCMC) estimation.
The results obtained indicate the presence of labour market discrimination in Albania and
Kosovo, but point to its absence in Bulgaria, Croatia, and Serbia. (authors' abstract)
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Leininger, Thomas J. "An Adaptive Bayesian Approach to Dose-Response Modeling." Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd3325.pdf.
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Kastner, Gregor, Sylvia Frühwirth-Schnatter, and Hedibert Freitas Lopes. "Efficient Bayesian Inference for Multivariate Factor Stochastic Volatility Models." WU Vienna University of Economics and Business, 2016. http://epub.wu.ac.at/4875/1/research_report_updated.pdf.
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We discuss efficient Bayesian estimation of dynamic covariance matrices in multivariate time series through a factor stochastic volatility model. In particular, we propose two interweaving strategies (Yu and Meng, Journal of Computational and Graphical Statistics, 20(3), 531-570, 2011) to substantially accelerate convergence and mixing of standard MCMC approaches. Similar to marginal data augmentation techniques, the proposed acceleration procedures exploit non-identifiability issues which frequently arise in factor models. Our new interweaving strategies are easy to implement and come at almost no extra computational cost; nevertheless, they can boost estimation efficiency by several orders of magnitude as is shown in extensive simulation studies. To conclude, the application of our algorithm to a 26-dimensional exchange rate data set illustrates the superior performance of the new approach for real-world data.Series: Research Report Series / Department of Statistics and Mathematics
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Andersson, Lovisa. "An application of Bayesian Hidden Markov Models to explore traffic flow conditions in an urban area." Thesis, Uppsala universitet, Statistiska institutionen, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-385187.
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This study employs Bayesian Hidden Markov Models as method to explore vehicle traffic flow conditions in an urban area in Stockholm, based on sensor data from separate road positions. Inter-arrival times are used as the observed sequences. These sequences of inter-arrival times are assumed to be generated from the distributions of four different (and hidden) traffic flow states; nightly free flow, free flow, mixture and congestion. The filtered and smoothed probability distributions of the hidden states and the most probable state sequences are obtained by using the forward, forward-backward and Viterbi algorithms. The No-U-Turn sampler is used to sample from the posterior distributions of all unknown parameters. The obtained results show in a satisfactory way that the Hidden Markov Models can detect different traffic flow conditions. Some of the models have problems with divergence, but the obtained results from those models still show satisfactory results. In fact, two of the models that converged seemed to overestimate the presence of congested traffic and all the models that not converged seem to do adequate estimations of the probability of being in a congested state. Since the interest of this study lies in estimating the current traffic flow condition, and not in doing parameter inference, the model choice of Bayesian Hidden Markov Models is satisfactory. Due to the unsupervised nature of the problematization of this study, it is difficult to evaluate the accuracy of the results. However, a model with simulated data and known states was also implemented, which resulted in a high classification accuracy. This indicates that the choice of Hidden Markov Models is a good model choice for estimating traffic flow conditions.
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Aslan, Sipan. "Comparison Of Missing Value Imputation Methods For Meteorological Time Series Data." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612426/index.pdf.
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Dealing with missing data in spatio-temporal time series constitutes important branch of general missing data problem. Since the statistical properties of time-dependent data characterized by sequentiality of observations then any interruption of consecutiveness in time series will cause severe problems. In order to make reliable analyses in this case missing data must be handled cautiously without disturbing the series statistical properties, mainly as temporal and spatial dependencies.
In this study we aimed to compare several imputation methods for the appropriate completion of missing values of the spatio-temporal meteorological time series. For this purpose, several missing imputation methods are assessed on their imputation performances for artificially created missing data in monthly total precipitation and monthly mean temperature series which are obtained from the climate stations of Turkish State Meteorological Service. Artificially created missing data are estimated by using six methods. Single Arithmetic Average (SAA), Normal Ratio (NR) and NR Weighted with Correlations (NRWC) are the three simple methods used in the study. On the other hand, we used two computational intensive methods for missing data imputation which are called Multi Layer Perceptron type Neural Network (MLPNN) and Monte Carlo Markov Chain based on Expectation-Maximization Algorithm (EM-MCMC). In addition to these, we propose a modification in the EM-MCMC method in which results of simple imputation methods are used as auxiliary variables. Beside the using accuracy measure based on squared errors we proposed Correlation Dimension (CD) technique for appropriate evaluation of imputation performances which is also important subject of Nonlinear Dynamic Time Series Analysis.
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Oulad, Ameziane Mehdi. "Amélioration de l'exploration de l'espace d'état dans les méthodes de Monte Carlo séquentielles pour le suivi visuel." Thesis, Ecole centrale de Lille, 2017. http://www.theses.fr/2017ECLI0007.
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Le suivi visuel constitue une tâche essentielle en vision par ordinateur. Les approches Bayésiennes sont largement utilisées aujourd’hui pour résoudre la problématique du suivi visuel. Notamment grâce aux possibilités offertes par les méthodes de Monte Carlo séquentielles (SMC) qui prennent en comptes les incertitudes du model et s’adaptent à des scenarios variés. L’efficacité des méthodes SMC dépend fortement du choix de la loi de proposition qui permet d’explorer l’espace d’état.Dans cette thèse, nous cherchons à améliorer l’exploration de l’espace d’état en approchant la loi de proposition optimale. Cette loi de proposition quasi-optimale repose sur une approximation de la fonction de vraisemblance, et ce en utilisant une information de détection souple qui est à la foi plus fiable et moins couteuse à calculer. En comparaison avec les travaux antérieurs sur le sujet, notre loi de proposition quasi-optimale offre un bon compromis entre l’optimalité et la complexité algorithmique. Améliorer l’exploration de l’espace d’état est nécessaire principalement dans deux applications du suivi visuel : Le suivi des mouvements abrupts et le suivi multi objet. Dans le cadre de cette thèse on a souligné la capacité des méthodes SMC quasi-optimales à traiter les mouvements abrupts, en les comparants aux méthodes proposées dans la littérature spécifiquement pour ce type de scenario. Aussi, on a étendu notre loi de proposition quasi-optimale pour le suivi multi objet et nous en avons démontré l’intérêt. Par ailleurs, on a implémenté le filtre particulaire Local qui partitionne l’espace d’état en sous-espaces indépendants de taille inférieure tout en modélisant des interactionsIn computer vision applications, visual tracking is an important and a fundamental task. Solving the tracking problematic based on a statistical formulation in the Bayesian framework has gained great interest in recent years due to the capabilities of the sequential Monte Carlo (SMC) methods to adapt to various tracking schemes and to take into account model uncertainties. In practice, the efficiency of SMC methods strongly depends on the proposal density used to explore the state space, thus the choice of the proposal is essential. In the thesis, our approach to efficiently explore the state space aims to derive a close approximation of the optimal proposal. The proposed near optimal proposal relies on an approximation of the likelihood using a new form of likelihood based on soft detection information which is more trustworthy and requires less calculations than the usual likelihood. In comparison with previous works, our near optimal proposal offers a good compromise between computational complexity and optimality.Improving the exploration of the state space is most required in two visual tracking applications: abrupt motion tracking and multiple object tracking. In the thesis, we focus on the ability of the near optimal SMC methods to deal with abrupt motion situations and we compare them to the state-of-the-art methods proposed in the literature for these situations. Also, we extend the near optimal proposal to multiple object tracking scenarios and show the benefit of using the near optimal SMC algorithms for these scenarios. Moreover, we implemented the Local PF which partition large state spaces into separate smaller subspaces while modelling interactions
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Silva, Maria Joseane Cruz da. "Imputação múltipla: comparação e eficiência em experimentos multiambientais." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-08082012-143901/.
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Em experimentos de genótipos ambiente são comuns à presença de valores ausentes, devido à quantidade insuficiente de genótipos para aplicação dificultando, por exemplo, o processo de recomendação de genótipos mais produtivos, pois para a aplicação da maioria das técnicas estatísticas multivariadas exigem uma matriz de dados completa. Desta forma, aplicam-se métodos que estimam os valores ausentes a partir dos dados disponíveis conhecidos como imputação de dados (simples e múltiplas), levando em consideração o padrão e o mecanismo de dados ausentes. O objetivo deste trabalho é avaliar a eficiência da imputação múltipla livre da distribuição (IMLD) (BERGAMO et al., 2008; BERGAMO, 2007) comparando-a com o método de imputação múltipla com Monte Carlo via cadeia de Markov (IMMCMC), na imputação de unidades ausentes presentes em experimentos de interação genótipo (25) ambiente (7). Estes dados são provenientes de um experimento aleatorizado em blocos com a cultura de Eucaluptus grandis (LAVORANTI, 2003), os quais foram feitas retiradas de porcentagens aleatoriamente (10%, 20%, 30%) e posteriormente imputadas pelos métodos considerados. Os resultados obtidos por cada método mostraram que, a eficiência relativa em ambas as porcentagens manteve-se acima de 90%, sendo menor para o ambiente (4) quando imputado com a IMLD. Para a medida geral de exatidão, a medida que ocorreu acréscimo de dados em falta, foi maior ao imputar os valores ausentes com a IMMCMC, já para o método IMLD estes valores variaram sendo menor a 20% de retirada aleatória. Dentre os resultados encontrados, é de suma importância considerar o fato de que o método IMMCMC considera a suposição de normalidade, já o método IMLD leva vantagem sobre este ponto, pois não considera restrição alguma sobre a distribuição dos dados nem sobre os mecanismos e padrões de ausência.In trials of genotypes by environment, the presence of absent values is common, due to the quantity of insufficiency of genotype application, making difficult for example, the process of recommendation of more productive genotypes, because for the application of the majority of the multivariate statistical techniques, a complete data matrix is required. Thus, methods that estimate the absent values from available data, known as imputation of data (simple and multiple) are applied, taking into consideration standards and mechanisms of absent data. The goal of this study is to evaluate the efficiency of multiple imputations free of distributions (IMLD) (BERGAMO et al., 2008; BERGAMO, 2007), compared with the Monte Carlo via Markov chain method of multiple imputation (IMMCMC), in the absent units present in trials of genotype interaction (25)environment (7). This data is provisional of random tests in blocks with Eucaluptus grandis cultures (LAVORANTI, 2003), of which random percentages of withdrawals (10%, 20%, 30%) were performed, with posterior imputation of the considered methods. The results obtained for each method show that, the relative efficiency in both percentages were maintained above 90%, being less for environmental (4) when imputed with an IMLD. The general measure of exactness, the measures where higher absent data occurred, was larger when absent values with an IMMCMC was imputed, as for the IMLD method, the varied absent values were lower at 20% for random withdrawals. Among results found, it is of sum importance to take into consideration the fact that the IMMCMC method considers it to be an assumption of normality, as for the IMLD method, it does not consider any restriction on the distribution of data, not on mechanisms and absent standards, which is an advantage on imputations.
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Behlouli, Abdeslam. "Simulation du canal optique sans fil. Application aux télécommunications optique sans fil." Thesis, Poitiers, 2016. http://www.theses.fr/2016POIT2308/document.
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Le contexte de cette thèse est celui des communications optiques sans fil pour des applications en environnements indoor. Pour discuter des performances d'une liaison optique sans fil, il est nécessaire d'établir une étude caractéristique du comportement du canal de propagation. Cette étude passe par l'étape de la mesure ou de l'estimation par la simulation de la réponse impulsionnelle. Après avoir décrit la composition d'une liaison et passé en revue les méthodes de simulation existantes, nous présentons nos algorithmes de simulation dans des environnements réalistes, en nous intéressant à leurs performances en termes de précision et de temps de calcul. Ces méthodes sont basées sur la résolution des équations de transport de la lumière par du lancer de rayons associées aux méthodes d'intégration stochastique de Monte Carlo. La version classique de ces méthodes est à la base de trois algorithmes de simulations proposés. En utilisant une optimisation par des chaînes de Markov, nous présentons ensuite deux autres algorithmes. Un bilan des performances de ces algorithmes est établi dans des scénarios mono et multi-antennes. Finalement, nous appliquons nos algorithmes pour caractériser l'impact de l'environnement de simulation sur les performances d'une liaison de communication par lumière visible, à savoir les modèles d'émetteurs, les matériaux des surfaces, l'obstruction du corps de l'utilisateur et sa mobilité, et la géométrie de la scène de simulationThe context of this PhD thesis falls within the scope of optical wireless communications for applications in indoor environments. To discuss the performance of an optical wireless link, it is necessary to establish a characteristic study of the behavior of the optical wave propagation channel. This study can be realized by measurement or by the simulation of the channel impulse response. After describing the composition of an optical wireless link and reviewing existing simulation methods, we present our new simulation algorithms channel in realistic environments by focusing on their performances in terms of accuracy and their complexity in terms of computation time. These methods are based on solving the light transport equations by ray-tracing techniques associated with stochastic Monte Carlo integration methods. The classical version of these methods is the basis of three proposed simulation algorithms. By applying an optimization using Markov Chain, we present two new algorithms. A performance assessment of our simulation algorithms is established in mono and multi-antenna scenarios of our simulation algorithms. Finally, we present the application of these algorithms for characterizing the impact of the simulation environment on the performances of a visible light communication link. We particularly focus on the transmitter models, surface coating materials, obstruction of the user's body and its mobility, and the geometry of the simulation scene
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Reynolds, Toby J. "Bayesian modelling of integrated data and its application to seabird populations." Thesis, University of St Andrews, 2010. http://hdl.handle.net/10023/1635.
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Integrated data analyses are becoming increasingly popular in studies of wild animal populations where two or more separate sources of data contain information about common parameters. Here we develop an integrated population model using abundance and demographic data from a study of common guillemots (Uria aalge) on the Isle of May, southeast Scotland. A state-space model for the count data is supplemented by three demographic time series (productivity and two mark-recapture-recovery (MRR)), enabling the estimation of prebreeder emigration rate - a parameter for which there is no direct observational data, and which is unidentifiable in the separate analysis of MRR data. A Bayesian approach using MCMC provides a flexible and powerful analysis framework. This model is extended to provide predictions of future population trajectories. Adopting random effects models for the survival and productivity parameters, we implement the MCMC algorithm to obtain a posterior sample of the underlying process means and variances (and population sizes) within the study period. Given this sample, we predict future demographic parameters, which in turn allows us to predict future population sizes and obtain the corresponding posterior distribution. Under the assumption that recent, unfavourable conditions persist in the future, we obtain a posterior probability of 70% that there is a population decline of >25% over a 10-year period. Lastly, using MRR data we test for spatial, temporal and age-related correlations in guillemot survival among three widely separated Scottish colonies that have varying overlap in nonbreeding distribution. We show that survival is highly correlated over time for colonies/age classes sharing wintering areas, and essentially uncorrelated for those with separate wintering areas. These results strongly suggest that one or more aspects of winter environment are responsible for spatiotemporal variation in survival of British guillemots, and provide insight into the factors driving multi-population dynamics of the species.
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Bakra, Eleni. "Aspects of population Markov chain Monte Carlo and reversible jump Markov chain Monte Carlo." Thesis, University of Glasgow, 2009. http://theses.gla.ac.uk/1247/.
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Calmet, Claire. "Inférences sur l'histoire des populations à partir de leur diversité génétique : étude de séquences démographiques de type fondation-explosion." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2002. http://tel.archives-ouvertes.fr/tel-00288526.
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L'étude de la démographie dans une perspective historique participe à la compréhension des processus évolutifs. Les données de diversité génétique sont potentiellement informatives quant au passé démographique des populations: en effet, ce passé est enregistré avec perte d'information par les marqueurs moléculaires, par l'intermédiaire de leur histoire généalogique et mutationnelle. L'acquisition de données de diversité génétique est de plus en plus rapide et aisée, et concerne potentiellement n'importe quel organisme d'intérêt. D'où un effort dans la dernière décennie pour développer les outils statistiques permettant d'extraire l'information démographique des données de typage génétique.La présente thèse propose une extension de la méthode d'inférence bayésienne développée en 1999 par M. Beaumont. Comme la méthode originale, (i) elle est basée sur le coalescent de Kingman avec variations d'effectif, (ii) elle utilise l'algorithme de Metropolis-Hastings pour échantillonner selon la loi a posteriori des paramètres d'intérêt et (iii) elle permet de traiter des données de typage à un ou plusieurs microsatellites indépendants. La version étendue généralise les modèles démographique et mutationnel supposés dans la méthode initiale: elle permet d'inférer les paramètres d'un modèle de fondation-explosion pour la population échantillonnée et d'un modèle mutationnel à deux phases, pour les marqueurs microsatellites typés. C'est la première fois qu'une méthode probabiliste exacte incorpore pour les microsatellites un modèle mutationnel autorisant des sauts.
Le modèle démographique et mutationnel est exploré. L'analyse de jeux de données simulés permet d'illustrer et de comparer la loi a posteriori des paramètres pour des scénarios historiques: par exemple une stabilité démographique, une croissance exponentielle et une fondation-explosion. Une typologie des lois a posteriori est proposée. Des recommandations sur l'effort de typage dans les études empiriques sont données: un unique marqueur microsatellite peut conduire à une loi a posteriori très structurée. Toutefois, les zones de forte densité a posteriori représentent des scénarios de différents types. 50 génomes haploides typés à 5 marqueurs microsatellites suffisent en revanche à détecter avec certitude (99% de la probabilité a posteriori) une histoire de fondation-explosion tranchée. Les conséquences de la violation des hypothèses du modèle démographique sont discutées, ainsi que les interactions entre processus et modèle mutationnel. En particulier, il est établi que le fait de supposer un processus mutationnel conforme au modèle SMM, alors que ce processus est de type TPM, peut générer un faux signal de déséquilibre génétique. La modélisation des sauts mutationnels permet de supprimer ce faux signal.
La méthode est succinctement appliquée à l'étude de deux histoires de fondation-explosion: l'introduction du chat Felis catus sur les îles Kerguelen et celle du surmulot Rattus norvegicus sur les îles du large de la Bretagne. Il est d'abord montré que la méthode fréquentiste développée par Cornuet et Luikart (1996) ne permet pas de détecter les fondations récentes et drastiques qu'ont connu ces populations. Cela est vraisemblablement dû à des effets contraires de la fondation et de l'explosion, sur les statistiques utilisées dans cette méthode.
La méthode bayésienne ne détecte pas non plus la fondation si l'on force une histoire démographique en marche d'escalier, pour la même raison. La fondation et l'explosion deviennent détectables si le modèle démographique les autorise. Toutefois, les dépendances entre les paramètres du modèle empêchent de les inférer marginalement avec précision. Toute information a priori sur un paramètre contraint fortement les valeurs des autres paramètres. Ce constat confirme le potentiel de populations d'histoire documentée pour l'estimation indirecte des paramètres d'un modèle de mutation des marqueurs.
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Holenstein, Roman. "Particle Markov chain Monte Carlo." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/7319.
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Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods have emerged as the two main tools to sample from high-dimensional probability distributions. Although asymptotic convergence of MCMC algorithms is ensured under weak assumptions, the performance of these latters is unreliable when the proposal distributions used to explore the space are poorly chosen and/or if highly correlated variables are updated independently. In this thesis we propose a new Monte Carlo framework in which we build efficient high-dimensional proposal distributions using SMC methods. This allows us to design effective MCMC algorithms in complex scenarios where standard strategies fail. We demonstrate these algorithms on a number of example problems, including simulated tempering, nonlinear non-Gaussian state-space model, and protein folding.
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Byrd, Jonathan Michael Robert. "Parallel Markov Chain Monte Carlo." Thesis, University of Warwick, 2010. http://wrap.warwick.ac.uk/3634/.
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The increasing availability of multi-core and multi-processor architectures provides new opportunities for improving the performance of many computer simulations. Markov Chain Monte Carlo (MCMC) simulations are widely used for approximate counting problems, Bayesian inference and as a means for estimating very highdimensional integrals. As such MCMC has found a wide variety of applications in fields including computational biology and physics,financial econometrics, machine learning and image processing. This thesis presents a number of new method for reducing the runtime of Markov Chain Monte Carlo simulations by using SMP machines and/or clusters. Two of the methods speculatively perform iterations in parallel, reducing the runtime of MCMC programs whilst producing statistically identical results to conventional sequential implementations. The other methods apply only to problem domains that can be presented as an image, and involve using various means of dividing the image into subimages that can be proceed with some degree of independence. Where possible the thesis includes a theoretical analysis of the reduction in runtime that may be achieved using our technique under perfect conditions, and in all cases the methods are tested and compared on selection of multi-core and multi-processor architectures. A framework is provided to allow easy construction of MCMC application that implement these parallelisation methods.
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Gomes, André Yoshizumi. "Família Weibull de razão de chances na presença de covariáveis." Universidade Federal de São Carlos, 2009. https://repositorio.ufscar.br/handle/ufscar/4558.
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Previous issue date: 2009-03-18Universidade Federal de Minas Gerais
The Weibull distribuition is a common initial choice for modeling data with monotone hazard rates. However, such distribution fails to provide a reasonable parametric _t when the hazard function is unimodal or bathtub-shaped. In this context, Cooray (2006) proposed a generalization of the Weibull family by considering the distributions of the odds of Weibull and inverse Weibull families, referred as the odd Weibull family which is not just useful for modeling unimodal and bathtub-shaped hazards, but it is also convenient for testing goodness-of-_t of Weibull and inverse Weibull as submodels. In this project we have systematically studied the odd Weibull family along with its properties, showing motivations for its utilization, inserting covariates in the model, pointing out some troubles associated with the maximum likelihood estimation and proposing interval estimation and hypothesis test construction methodologies for the model parameters. We have also compared resampling results with asymptotic ones. Coverage probability from proposed con_dence intervals and size and power of considered hypothesis tests were both analyzed as well via Monte Carlo simulation. Furthermore, we have proposed a Bayesian estimation methodology for the model parameters based in Monte Carlo Markov Chain (MCMC) simulation techniques.
A distribuição Weibull é uma escolha inicial freqüente para modelagem de dados com taxas de risco monótonas. Entretanto, esta distribuição não fornece um ajuste paramétrico razoável quando as funções de risco assumem um formato unimodal ou em forma de banheira. Neste contexto, Cooray (2006) propôs uma generalização da família Weibull considerando a distribuição da razão de chances das famílias Weibull e Weibull inversa, referida como família Weibull de razão de chances. Esta família não é apenas conveniente para modelar taxas de risco unimodal e banheira, mas também é adequada para testar a adequabilidade do ajuste das famílias Weibull e Weibull inversa como submodelos. Neste trabalho, estudamos sistematicamente a família Weibull de razão de chances e suas propriedades, apontando as motivações para o seu uso, inserindo covariáveis no modelo, veri_cando as di_culdades referentes ao problema da estimação de máxima verossimilhança dos parâmetros do modelo e propondo metodologia de estimação intervalar e construção de testes de hipóteses para os parâmetros do modelo. Comparamos os resultados obtidos por meio dos métodos de reamostragem com os resultados obtidos via teoria assintótica. Tanto a probabilidade de cobertura dos intervalos de con_ança propostos quanto o tamanho e poder dos testes de hipóteses considerados foram estudados via simulação de Monte Carlo. Além disso, propusemos uma metodologia Bayesiana de estimação para os parâmetros do modelo baseados em técnicas de simulação de Monte Carlo via Cadeias de Markov.
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Zhang, Yichuan. "Scalable geometric Markov chain Monte Carlo." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/20978.
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Markov chain Monte Carlo (MCMC) is one of the most popular statistical inference methods in machine learning. Recent work shows that a significant improvement of the statistical efficiency of MCMC on complex distributions can be achieved by exploiting geometric properties of the target distribution. This is known as geometric MCMC. However, many such methods, like Riemannian manifold Hamiltonian Monte Carlo (RMHMC), are computationally challenging to scale up to high dimensional distributions. The primary goal of this thesis is to develop novel geometric MCMC methods applicable to large-scale problems. To overcome the computational bottleneck of computing second order derivatives in geometric MCMC, I propose an adaptive MCMC algorithm using an efficient approximation based on Limited memory BFGS. I also propose a simplified variant of RMHMC that is able to work effectively on larger scale than the previous methods. Finally, I address an important limitation of geometric MCMC, namely that is only available for continuous distributions. I investigate a relaxation of discrete variables to continuous variables that allows us to apply the geometric methods. This is a new direction of MCMC research which is of potential interest to many applications. The effectiveness of the proposed methods is demonstrated on a wide range of popular models, including generalised linear models, conditional random fields (CRFs), hierarchical models and Boltzmann machines.
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Fang, Youhan. "Efficient Markov Chain Monte Carlo Methods." Thesis, Purdue University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10809188.
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Generating random samples from a prescribed distribution is one of the most important and challenging problems in machine learning, Bayesian statistics, and the simulation of materials. Markov Chain Monte Carlo (MCMC) methods are usually the required tool for this task, if the desired distribution is known only up to a multiplicative constant. Samples produced by an MCMC method are real values in N-dimensional space, called the configuration space. The distribution of such samples converges to the target distribution in the limit. However, existing MCMC methods still face many challenges that are not well resolved. Difficulties for sampling by using MCMC methods include, but not exclusively, dealing with high dimensional and multimodal problems, high computation cost due to extremely large datasets in Bayesian machine learning models, and lack of reliable indicators for detecting convergence and measuring the accuracy of sampling. This dissertation focuses on new theory and methodology for efficient MCMC methods that aim to overcome the aforementioned difficulties.
One contribution of this dissertation is generalizations of hybrid Monte Carlo (HMC). An HMC method combines a discretized dynamical system in an extended space, called the state space, and an acceptance test based on the Metropolis criterion. The discretized dynamical system used in HMC is volume preserving—meaning that in the state space, the absolute Jacobian of a map from one point on the trajectory to another is 1. Volume preservation is, however, not necessary for the general purpose of sampling. A general theory allowing the use of non-volume preserving dynamics for proposing MCMC moves is proposed. Examples including isokinetic dynamics and variable mass Hamiltonian dynamics with an explicit integrator, are all designed with fewer restrictions based on the general theory. Experiments show improvement in efficiency for sampling high dimensional multimodal problems. A second contribution is stochastic gradient samplers with reduced bias. An in-depth analysis of the noise introduced by the stochastic gradient is provided. Two methods to reduce the bias in the distribution of samples are proposed. One is to correct the dynamics by using an estimated noise based on subsampled data, and the other is to introduce additional variables and corresponding dynamics to adaptively reduce the bias. Extensive experiments show that both methods outperform existing methods. A third contribution is quasi-reliable estimates of effective sample size. Proposed is a more reliable indicator—the longest integrated autocorrelation time over all functions in the state space—for detecting the convergence and measuring the accuracy of MCMC methods. The superiority of the new indicator is supported by experiments on both synthetic and real problems.
Minor contributions include a general framework of changing variables, and a numerical integrator for the Hamiltonian dynamics with fourth order accuracy. The idea of changing variables is to transform the potential energy function as a function of the original variable to a function of the new variable, such that undesired properties can be removed. Two examples are provided and preliminary experimental results are obtained for supporting this idea. The fourth order integrator is constructed by combining the idea of the simplified Takahashi-Imada method and a two-stage Hessian-based integrator. The proposed method, called two-stage simplified Takahashi-Imada method, shows outstanding performance over existing methods in high-dimensional sampling problems.
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Neuhoff, Daniel. "Reversible Jump Markov Chain Monte Carlo." Doctoral thesis, Humboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät, 2016. http://dx.doi.org/10.18452/17461.
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Die vier in der vorliegenden Dissertation enthaltenen Studien beschäftigen sich vorwiegend mit dem dynamischen Verhalten makroökonomischer Zeitreihen. Diese Dynamiken werden sowohl im Kontext eines einfachen DSGE Modells, als auch aus der Sichtweise reiner Zeitreihenmodelle untersucht.The four studies of this thesis are concerned predominantly with the dynamics of macroeconomic time series, both in the context of a simple DSGE model, as well as from a pure time series modeling perspective.
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Strid, Ingvar. "Computational methods for Bayesian inference in macroeconomic models." Doctoral thesis, Handelshögskolan i Stockholm, Ekonomisk Statistik (ES), 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hhs:diva-1118.
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The New Macroeconometrics may succinctly be described as the application of Bayesian analysis to the class of macroeconomic models called Dynamic Stochastic General Equilibrium (DSGE) models. A prominent local example from this research area is the development and estimation of the RAMSES model, the main macroeconomic model in use at Sveriges Riksbank. Bayesian estimation of DSGE models is often computationally demanding. In this thesis fast algorithms for Bayesian inference are developed and tested in the context of the state space model framework implied by DSGE models. The algorithms discussed in the thesis deal with evaluation of the DSGE model likelihood function and sampling from the posterior distribution. Block Kalman filter algorithms are suggested for likelihood evaluation in large linearised DSGE models. Parallel particle filter algorithms are presented for likelihood evaluation in nonlinearly approximated DSGE models. Prefetching random walk Metropolis algorithms and adaptive hybrid sampling algorithms are suggested for posterior sampling. The generality of the algorithms, however, suggest that they should be of interest also outside the realm of macroeconometrics.