Relevant bibliographies by topics / Gaussian; Markov chain Monte Carlo methods

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Gaussian; Markov chain Monte Carlo methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Gaussian; Markov chain Monte Carlo methods.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Gaussian; Markov chain Monte Carlo methods"

1

Wang, Zheng, Shanxiang Lyu, and Ling Liu. "Learnable Markov Chain Monte Carlo Sampling Methods for Lattice Gaussian Distribution." IEEE Access 7 (2019): 87494–503. http://dx.doi.org/10.1109/access.2019.2925530.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Ahmadian, Yashar, Jonathan W. Pillow, and Liam Paninski. "Efficient Markov Chain Monte Carlo Methods for Decoding Neural Spike Trains." Neural Computation 23, no. 1 (January 2011): 46–96. http://dx.doi.org/10.1162/neco_a_00059.

Full text
Abstract:
Stimulus reconstruction or decoding methods provide an important tool for understanding how sensory and motor information is represented in neural activity. We discuss Bayesian decoding methods based on an encoding generalized linear model (GLM) that accurately describes how stimuli are transformed into the spike trains of a group of neurons. The form of the GLM likelihood ensures that the posterior distribution over the stimuli that caused an observed set of spike trains is log concave so long as the prior is. This allows the maximum a posteriori (MAP) stimulus estimate to be obtained using efficient optimization algorithms. Unfortunately, the MAP estimate can have a relatively large average error when the posterior is highly nongaussian. Here we compare several Markov chain Monte Carlo (MCMC) algorithms that allow for the calculation of general Bayesian estimators involving posterior expectations (conditional on model parameters). An efficient version of the hybrid Monte Carlo (HMC) algorithm was significantly superior to other MCMC methods for gaussian priors. When the prior distribution has sharp edges and corners, on the other hand, the “hit-and-run” algorithm performed better than other MCMC methods. Using these algorithms, we show that for this latter class of priors, the posterior mean estimate can have a considerably lower average error than MAP, whereas for gaussian priors, the two estimators have roughly equal efficiency. We also address the application of MCMC methods for extracting nonmarginal properties of the posterior distribution. For example, by using MCMC to calculate the mutual information between the stimulus and response, we verify the validity of a computationally efficient Laplace approximation to this quantity for gaussian priors in a wide range of model parameters; this makes direct model-based computation of the mutual information tractable even in the case of large observed neural populations, where methods based on binning the spike train fail. Finally, we consider the effect of uncertainty in the GLM parameters on the posterior estimators.
APA, Harvard, Vancouver, ISO, and other styles
3

Wang, Zheng. "Markov Chain Monte Carlo Methods for Lattice Gaussian Sampling: Convergence Analysis and Enhancement." IEEE Transactions on Communications 67, no. 10 (October 2019): 6711–24. http://dx.doi.org/10.1109/tcomm.2019.2926470.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Whiley, Matt, and Simon P. Wilson. "Parallel algorithms for Markov chain Monte Carlo methods in latent spatial Gaussian models." Statistics and Computing 14, no. 3 (August 2004): 171–79. http://dx.doi.org/10.1023/b:stco.0000035299.51541.5e.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Geweke, John, and Hisashi Tanizaki. "On markov chain monte carlo methods for nonlinear and non-gaussian state-space models." Communications in Statistics - Simulation and Computation 28, no. 4 (January 1999): 867–94. http://dx.doi.org/10.1080/03610919908813583.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Jiao, Zhun, and Rong Zhang. "Improved Particle Filter for Integrated Navigation System." Applied Mechanics and Materials 543-547 (March 2014): 1278–81. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1278.

Full text
Abstract:
As a new method for dealing with any nonlinear or non-Gaussian distributions, based on the Monte Carlo methods and Bayesian filtering, particle filters (PF) are favored by researchers and widely applied in many fields. Based on particle filtering, an improved particle filter (IPF) proposal distribution is presented. Evaluation of the weights is simplified and other improved techniques including the residual resampling step and Markov Chain Monte Carlo method are introduced for SINS/GPS integrated navigation system. The simulation results confirm that the improved particle filter outperforms the others.
APA, Harvard, Vancouver, ISO, and other styles
7

Lu, Dan, Daniel Ricciuto, Anthony Walker, Cosmin Safta, and William Munger. "Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods." Biogeosciences 14, no. 18 (September 27, 2017): 4295–314. http://dx.doi.org/10.5194/bg-14-4295-2017.

Full text
Abstract:
Abstract. Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this work, a differential evolution adaptive Metropolis (DREAM) algorithm is used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The calibration of DREAM results in a better model fit and predictive performance compared to the popular adaptive Metropolis (AM) scheme. Moreover, DREAM indicates that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identifies one mode. The application suggests that DREAM is very suitable to calibrate complex terrestrial ecosystem models, where the uncertain parameter size is usually large and existence of local optima is always a concern. In addition, this effort justifies the assumptions of the error model used in Bayesian calibration according to the residual analysis. The result indicates that a heteroscedastic, correlated, Gaussian error model is appropriate for the problem, and the consequent constructed likelihood function can alleviate the underestimation of parameter uncertainty that is usually caused by using uncorrelated error models.
APA, Harvard, Vancouver, ISO, and other styles
8

Durante, Daniele. "Conjugate Bayes for probit regression via unified skew-normal distributions." Biometrika 106, no. 4 (August 25, 2019): 765–79. http://dx.doi.org/10.1093/biomet/asz034.

Full text
Abstract:
Summary Regression models for dichotomous data are ubiquitous in statistics. Besides being useful for inference on binary responses, these methods serve as building blocks in more complex formulations, such as density regression, nonparametric classification and graphical models. Within the Bayesian framework, inference proceeds by updating the priors for the coefficients, typically taken to be Gaussians, with the likelihood induced by probit or logit regressions for the responses. In this updating, the apparent absence of a tractable posterior has motivated a variety of computational methods, including Markov chain Monte Carlo routines and algorithms that approximate the posterior. Despite being implemented routinely, Markov chain Monte Carlo strategies have mixing or time-inefficiency issues in large-$p$ and small-$n$ studies, whereas approximate routines fail to capture the skewness typically observed in the posterior. In this article it is proved that the posterior distribution for the probit coefficients has a unified skew-normal kernel under Gaussian priors. This result allows efficient Bayesian inference for a wide class of applications, especially in large-$p$ and small-to-moderate-$n$ settings where state-of-the-art computational methods face notable challenges. These advances are illustrated in a genetic study, and further motivate the development of a wider class of conjugate priors for probit models, along with methods for obtaining independent and identically distributed samples from the unified skew-normal posterior.
APA, Harvard, Vancouver, ISO, and other styles
9

Peng, Li Feng, Guo Shao Su, and Wei Zhao. "Fast Analysis of Structural Reliability Using Gaussian Process Classification Based Dynamic Response Surface Method." Applied Mechanics and Materials 501-504 (January 2014): 1067–70. http://dx.doi.org/10.4028/www.scientific.net/amm.501-504.1067.

Full text
Abstract:
The performance function of large-scale complicated engineering structure is always highly nonlinear and implicit, and its reliability needs to be evaluated through a time-consuming Finite Element method (FEM). A new method, Gaussian process classification (GPC) dynamic response surface based on Monte Carlo Simulation (MCS) was proposed. Small training samples were created using FEM and Markov chain. Then, the most probable point (MPP) is predicted quickly using MCS without any extra FEM analysis. Furthermore, an iterative algorithm is presented to reduce the errors of GPC by using information of MPP to improve the reconstructing precision constantly. Then, Monte Carlo method combined with GPC surface is applied to get the probability of failure. Several examples results demonstrate the efficiency and robustness of the proposed method, compared with the results of common reliability methods.
APA, Harvard, Vancouver, ISO, and other styles
10

Tilmann, F. J., H. Sadeghisorkhani, and A. Mauerberger. "Another look at the treatment of data uncertainty in Markov chain Monte Carlo inversion and other probabilistic methods." Geophysical Journal International 222, no. 1 (May 6, 2020): 388–405. http://dx.doi.org/10.1093/gji/ggaa168.

Full text
Abstract:
SUMMARY In probabilistic Bayesian inversions, data uncertainty is a crucial parameter for quantifying the uncertainties and correlations of the resulting model parameters or, in transdimensional approaches, even the complexity of the model. However, in many geophysical inference problems it is poorly known. Therefore, it is common practice to allow the data uncertainty itself to be a parameter to be determined. Although in principle any arbitrary uncertainty distribution can be assumed, Gaussian distributions whose standard deviation is then the unknown parameter to be estimated are the usual choice. In this special case, the paper demonstrates that a simple analytical integration is sufficient to marginalise out this uncertainty parameter, reducing the complexity of the model space without compromising the accuracy of the posterior model probability distribution. However, it is well known that the distribution of geophysical measurement errors, although superficially similar to a Gaussian distribution, typically contains more frequent samples along the tail of the distribution, so-called outliers. In linearized inversions these are often removed in subsequent iterations based on some threshold criterion, but in Markov chain Monte Carlo (McMC) inversions this approach is not possible as they rely on the likelihood ratios, which cannot be formed if the number of data points varies between the steps of the Markov chain. The flexibility to define the data error probability distribution in McMC can be exploited in order to account for this pattern of uncertainties in a natural way, without having to make arbitrary choices regarding residual thresholds. In particular, we can regard the data uncertainty distribution as a mixture between a Gaussian distribution, which represent valid measurements with some measurement error, and a uniform distribution, which represents invalid measurements. The relative balance between them is an unknown parameter to be estimated alongside the standard deviation of the Gauss distribution. For each data point, the algorithm can then assign a probability to be an outlier, and the influence of each data point will be effectively downgraded according to its probability to be an outlier. Furthermore, this assignment can change as the McMC search is exploring different parts of the model space. The approach is demonstrated with both synthetic and real tomography examples. In a synthetic test, the proposed mixed measurement error distribution allows recovery of the underlying model even in the presence of 6 per cent outliers, which completely destroy the ability of a regular McMC or linear search to provide a meaningful image. Applied to an actual ambient noise tomography study based on automatically picked dispersion curves, the resulting model is shown to be much more consistent for different data sets, which differ in the applied quality criteria, while retaining the ability to recover strong anomalies in selected parts of the model.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Gaussian; Markov chain Monte Carlo methods"

1

Manrique, Garcia Aurora. "Econometric analysis of limited dependent time series." Thesis, University of Oxford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.389797.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Vaičiulytė, Ingrida. "Study and application of Markov chain Monte Carlo method." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20141209_112440-55390.

Full text
Abstract:
Markov chain Monte Carlo adaptive methods by creating computationally effective algorithms for decision-making of data analysis with the given accuracy are analyzed in this dissertation. The tasks for estimation of parameters of the multivariate distributions which are constructed in hierarchical way (skew t distribution, Poisson-Gaussian model, stable symmetric vector law) are described and solved in this research. To create the adaptive MCMC procedure, the sequential generating method is applied for Monte Carlo samples, introducing rules for statistical termination and for sample size regulation of Markov chains. Statistical tasks, solved by this method, reveal characteristics of relevant computational problems including MCMC method. Effectiveness of the MCMC algorithms is analyzed by statistical modeling method, constructed in the dissertation. Tests made with sportsmen data and financial data of enterprises, belonging to health-care industry, confirmed that numerical properties of the method correspond to the theoretical model. The methods and algorithms created also are applied to construct the model for sociological data analysis. Tests of algorithms have shown that adaptive MCMC algorithm allows to obtain estimators of examined distribution parameters in lower number of chains, and reducing the volume of calculations approximately two times. The algorithms created in this dissertation can be used to test the systems of stochastic type and to solve other statistical... [to full text]
Disertacijoje nagrinėjami Markovo grandinės Monte-Karlo (MCMC) adaptavimo metodai, skirti efektyviems skaitiniams duomenų analizės sprendimų priėmimo su iš anksto nustatytu patikimumu algoritmams sudaryti. Suformuluoti ir išspręsti hierarchiniu būdu sudarytų daugiamačių skirstinių (asimetrinio t skirstinio, Puasono-Gauso modelio, stabiliojo simetrinio vektoriaus dėsnio) parametrų vertinimo uždaviniai. Adaptuotai MCMC procedūrai sukurti yra pritaikytas nuoseklaus Monte-Karlo imčių generavimo metodas, įvedant statistinį stabdymo kriterijų ir imties tūrio reguliavimą. Statistiniai uždaviniai išspręsti šiuo metodu leidžia atskleisti aktualias MCMC metodų skaitmeninimo problemų ypatybes. MCMC algoritmų efektyvumas tiriamas pasinaudojant disertacijoje sudarytu statistinio modeliavimo metodu. Atlikti eksperimentai su sportininkų duomenimis ir sveikatos industrijai priklausančių įmonių finansiniais duomenimis patvirtino, kad metodo skaitinės savybės atitinka teorinį modelį. Taip pat sukurti metodai ir algoritmai pritaikyti sociologinių duomenų analizės modeliui sudaryti. Atlikti tyrimai parodė, kad adaptuotas MCMC algoritmas leidžia gauti nagrinėjamų skirstinių parametrų įvertinius per mažesnį grandžių skaičių ir maždaug du kartus sumažinti skaičiavimų apimtį. Disertacijoje sukonstruoti algoritmai gali būti pritaikyti stochastinio pobūdžio sistemų tyrimui ir kitiems statistikos uždaviniams spręsti MCMC metodu.
APA, Harvard, Vancouver, ISO, and other styles
3

Lopez, lopera Andres Felipe. "Gaussian Process Modelling under Inequality Constraints." Thesis, Lyon, 2019. https://tel.archives-ouvertes.fr/tel-02863891.

Full text
Abstract:
Le conditionnement de processus gaussiens (PG) par des contraintes d’inégalité permet d’obtenir des modèles plus réalistes. Cette thèse s’intéresse au modèle de type PG proposé par maatouk (2015), obtenu par approximation finie, qui garantit que les contraintes sont satisfaites dans tout l’espace. Plusieurs contributions sont apportées. Premièrement, nous étudions l’emploi de méthodes de monte carlo par chaı̂nes de markov pour des lois multinormales tronquées. Elles fournissent un échantillonnage efficacpour des contraintes d’inégalité linéaires. Deuxièmement, nous explorons l’extension du modèle, jusque-làlimité à la dimension trois, à de plus grandes dimensions. Nous remarquons que l’introduction d’un bruit d’observations permet de monter à la dimension cinq. Nous proposons un algorithme d’insertion des nœuds, qui concentre le budget de calcul sur les dimensions les plus actives. Nous explorons aussi la triangulation de delaunay comme alternative à la tensorisation. Enfin, nous étudions l’utilisation de modèles additifs dans ce contexte, théoriquement et sur des problèmes de plusieurs centaines de variables. Troisièmement, nous donnons des résultats théoriques sur l’inférence sous contraintes d’inégalité. La consistance et la normalité asymptotique d’estimateurs par maximum de vraisemblance sont établies. L’ensemble des travaux a fait l’objet d’un développement logiciel en R. Ils sont appliqués à des problèmes de gestion des risques en sûreté nucléaire et inondations côtières, avec des contraintes de positivité et monotonie. Comme ouverture, nous montrons que la méthodologie fournit un cadre original pour l’étude de processus de Poisson d’intensité stochastique
Conditioning Gaussian processes (GPs) by inequality constraints gives more realistic models. This thesis focuses on the finite-dimensional approximation of GP models proposed by Maatouk (2015), which satisfies the constraints everywhere in the input space. Several contributions are provided. First, we study the use of Markov chain Monte Carlo methods for truncated multinormals. They result in efficient sampling for linear inequality constraints. Second, we explore the extension of the model, previously limited up tothree-dimensional spaces, to higher dimensions. The introduction of a noise effect allows us to go up to dimension five. We propose a sequential algorithm based on knot insertion, which concentrates the computational budget on the most active dimensions. We also explore the Delaunay triangulation as an alternative to tensorisation. Finally, we study the case of additive models in this context, theoretically and on problems involving hundreds of input variables. Third, we give theoretical results on inference under inequality constraints. The asymptotic consistency and normality of maximum likelihood estimators are established. The main methods throughout this manuscript are implemented in R language programming.They are applied to risk assessment problems in nuclear safety and coastal flooding, accounting for positivity and monotonicity constraints. As a by-product, we also show that the proposed GP approach provides an original framework for modelling Poisson processes with stochastic intensities
APA, Harvard, Vancouver, ISO, and other styles
4

Dahlin, Johan. "Accelerating Monte Carlo methods for Bayesian inference in dynamical models." Doctoral thesis, Linköpings universitet, Reglerteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-125992.

Full text
Abstract:
Making decisions and predictions from noisy observations are two important and challenging problems in many areas of society. Some examples of applications are recommendation systems for online shopping and streaming services, connecting genes with certain diseases and modelling climate change. In this thesis, we make use of Bayesian statistics to construct probabilistic models given prior information and historical data, which can be used for decision support and predictions. The main obstacle with this approach is that it often results in mathematical problems lacking analytical solutions. To cope with this, we make use of statistical simulation algorithms known as Monte Carlo methods to approximate the intractable solution. These methods enjoy well-understood statistical properties but are often computational prohibitive to employ. The main contribution of this thesis is the exploration of different strategies for accelerating inference methods based on sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC). That is, strategies for reducing the computational effort while keeping or improving the accuracy. A major part of the thesis is devoted to proposing such strategies for the MCMC method known as the particle Metropolis-Hastings (PMH) algorithm. We investigate two strategies: (i) introducing estimates of the gradient and Hessian of the target to better tailor the algorithm to the problem and (ii) introducing a positive correlation between the point-wise estimates of the target. Furthermore, we propose an algorithm based on the combination of SMC and Gaussian process optimisation, which can provide reasonable estimates of the posterior but with a significant decrease in computational effort compared with PMH. Moreover, we explore the use of sparseness priors for approximate inference in over-parametrised mixed effects models and autoregressive processes. This can potentially be a practical strategy for inference in the big data era. Finally, we propose a general method for increasing the accuracy of the parameter estimates in non-linear state space models by applying a designed input signal.
Borde Riksbanken höja eller sänka reporäntan vid sitt nästa möte för att nå inflationsmålet? Vilka gener är förknippade med en viss sjukdom? Hur kan Netflix och Spotify veta vilka filmer och vilken musik som jag vill lyssna på härnäst? Dessa tre problem är exempel på frågor där statistiska modeller kan vara användbara för att ge hjälp och underlag för beslut. Statistiska modeller kombinerar teoretisk kunskap om exempelvis det svenska ekonomiska systemet med historisk data för att ge prognoser av framtida skeenden. Dessa prognoser kan sedan användas för att utvärdera exempelvis vad som skulle hända med inflationen i Sverige om arbetslösheten sjunker eller hur värdet på mitt pensionssparande förändras när Stockholmsbörsen rasar. Tillämpningar som dessa och många andra gör statistiska modeller viktiga för många delar av samhället. Ett sätt att ta fram statistiska modeller bygger på att kontinuerligt uppdatera en modell allteftersom mer information samlas in. Detta angreppssätt kallas för Bayesiansk statistik och är särskilt användbart när man sedan tidigare har bra insikter i modellen eller tillgång till endast lite historisk data för att bygga modellen. En nackdel med Bayesiansk statistik är att de beräkningar som krävs för att uppdatera modellen med den nya informationen ofta är mycket komplicerade. I sådana situationer kan man istället simulera utfallet från miljontals varianter av modellen och sedan jämföra dessa mot de historiska observationerna som finns till hands. Man kan sedan medelvärdesbilda över de varianter som gav bäst resultat för att på så sätt ta fram en slutlig modell. Det kan därför ibland ta dagar eller veckor för att ta fram en modell. Problemet blir särskilt stort när man använder mer avancerade modeller som skulle kunna ge bättre prognoser men som tar för lång tid för att bygga. I denna avhandling använder vi ett antal olika strategier för att underlätta eller förbättra dessa simuleringar. Vi föreslår exempelvis att ta hänsyn till fler insikter om systemet och därmed minska antalet varianter av modellen som behöver undersökas. Vi kan således redan utesluta vissa modeller eftersom vi har en bra uppfattning om ungefär hur en bra modell ska se ut. Vi kan också förändra simuleringen så att den enklare rör sig mellan olika typer av modeller. På detta sätt utforskas rymden av alla möjliga modeller på ett mer effektivt sätt. Vi föreslår ett antal olika kombinationer och förändringar av befintliga metoder för att snabba upp anpassningen av modellen till observationerna. Vi visar att beräkningstiden i vissa fall kan minska ifrån några dagar till någon timme. Förhoppningsvis kommer detta i framtiden leda till att man i praktiken kan använda mer avancerade modeller som i sin tur resulterar i bättre prognoser och beslut.
APA, Harvard, Vancouver, ISO, and other styles
5

Vaičiulytė, Ingrida. "Markovo grandinės Monte-Karlo metodo tyrimas ir taikymas." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2014~D_20141209_112429-75205.

Full text
Abstract:
Disertacijoje nagrinėjami Markovo grandinės Monte-Karlo (MCMC) adaptavimo metodai, skirti efektyviems skaitiniams duomenų analizės sprendimų priėmimo su iš anksto nustatytu patikimumu algoritmams sudaryti. Suformuluoti ir išspręsti hierarchiniu būdu sudarytų daugiamačių skirstinių (asimetrinio t skirstinio, Puasono-Gauso modelio, stabiliojo simetrinio vektoriaus dėsnio) parametrų vertinimo uždaviniai. Adaptuotai MCMC procedūrai sukurti yra pritaikytas nuoseklaus Monte-Karlo imčių generavimo metodas, įvedant statistinį stabdymo kriterijų ir imties tūrio reguliavimą. Statistiniai uždaviniai išspręsti šiuo metodu leidžia atskleisti aktualias MCMC metodų skaitmeninimo problemų ypatybes. MCMC algoritmų efektyvumas tiriamas pasinaudojant disertacijoje sudarytu statistinio modeliavimo metodu. Atlikti eksperimentai su sportininkų duomenimis ir sveikatos industrijai priklausančių įmonių finansiniais duomenimis patvirtino, kad metodo skaitinės savybės atitinka teorinį modelį. Taip pat sukurti metodai ir algoritmai pritaikyti sociologinių duomenų analizės modeliui sudaryti. Atlikti tyrimai parodė, kad adaptuotas MCMC algoritmas leidžia gauti nagrinėjamų skirstinių parametrų įvertinius per mažesnį grandžių skaičių ir maždaug du kartus sumažinti skaičiavimų apimtį. Disertacijoje sukonstruoti algoritmai gali būti pritaikyti stochastinio pobūdžio sistemų tyrimui ir kitiems statistikos uždaviniams spręsti MCMC metodu.
Markov chain Monte Carlo adaptive methods by creating computationally effective algorithms for decision-making of data analysis with the given accuracy are analyzed in this dissertation. The tasks for estimation of parameters of the multivariate distributions which are constructed in hierarchical way (skew t distribution, Poisson-Gaussian model, stable symmetric vector law) are described and solved in this research. To create the adaptive MCMC procedure, the sequential generating method is applied for Monte Carlo samples, introducing rules for statistical termination and for sample size regulation of Markov chains. Statistical tasks, solved by this method, reveal characteristics of relevant computational problems including MCMC method. Effectiveness of the MCMC algorithms is analyzed by statistical modeling method, constructed in the dissertation. Tests made with sportsmen data and financial data of enterprises, belonging to health-care industry, confirmed that numerical properties of the method correspond to the theoretical model. The methods and algorithms created also are applied to construct the model for sociological data analysis. Tests of algorithms have shown that adaptive MCMC algorithm allows to obtain estimators of examined distribution parameters in lower number of chains, and reducing the volume of calculations approximately two times. The algorithms created in this dissertation can be used to test the systems of stochastic type and to solve other statistical... [to full text]
APA, Harvard, Vancouver, ISO, and other styles
6

Puengnim, Anchalee. "Classification de modulations linéaires et non-linéaires à l'aide de méthodes bayésiennes." Toulouse, INPT, 2008. http://ethesis.inp-toulouse.fr/archive/00000676/.

Full text
Abstract:
La reconnaissance de modulations numériques consiste à identifier, au niveau du récepteur d'une chaîne de transmission, l'alphabet auquel appartiennent les symboles du message transmis. Cette reconnaissance est nécessaire dans de nombreux scénarios de communication, afin, par exemple, de sécuriser les transmissions pour détecter d'éventuels utilisateurs non autorisés ou bien encore de déterminer quel terminal brouille les autres. Le signal observé en réception est généralement affecté d'un certain nombre d'imperfections, dues à une synchronisation imparfaite de l'émetteur et du récepteur, une démodulation imparfaite, une égalisation imparfaite du canal de transmission. Nous proposons plusieurs méthodes de classification qui permettent d'annuler les effets liés aux imperfections de la chaîne de transmission. Les symboles reçus sont alors corrigés puis comparés à ceux du dictionnaire des symboles transmis
This thesis studies classification of digital linear and nonlinear modulations using Bayesian methods. Modulation recognition consists of identifying, at the receiver, the type of modulation signals used by the transmitter. It is important in many communication scenarios, for example, to secure transmissions by detecting unauthorized users, or to determine which transmitter interferes the others. The received signal is generally affected by a number of impairments. We propose several classification methods that can mitigate the effects related to imperfections in transmission channels. More specifically, we study three techniques to estimate the posterior probabilities of the received signals conditionally to each modulation
APA, Harvard, Vancouver, ISO, and other styles
7

Fang, Youhan. "Efficient Markov Chain Monte Carlo Methods." Thesis, Purdue University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10809188.

Full text
Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles
8

Murray, Iain Andrew. "Advances in Markov chain Monte Carlo methods." Thesis, University College London (University of London), 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487199.

Full text
Abstract:
Probability distributions over many variables occur frequently in Bayesian inference, statistical physics and simulation studies. Samples from distributions give insight into their typical behavior and can allow approximation of any quantity of interest, such as expectations or normalizing constants. Markov chain Monte Carlo (MCMC), introduced by Metropolis et al. (1953), allows r sampling from distributions with intractable normalization, and remains one of most important tools for approximate computation with probability distributions. I While not needed by MCMC, normalizers are key quantities: in Bayesian statistics marginal likelihoods are needed for model comparison; in statistical physics many physical quantities relate to the partition function. In this thesis we propose and investigate several new Monte Carlo algorithms, both for evaluating normalizing constants and for improved sampling of distributions. Many MCMC correctness proofs rely on using reversible transition operators; this can lead to chains exploring by slow random walks. After reviewing existing MCMC algorithms, we develop a new framework for constructing non-reversible transition operators from existing reversible ones. Next we explore and extend MCMC-based algorithms for computing normalizing constants. In particular we develop a newMCMC operator and Nested Sampling approach for the Potts model. Our results demonstrate that these approaches can be superior to finding normalizing constants by annealing methods and can obtain better posterior samples. Finally we consider 'doubly-intractable' distributions with extra unknown normalizer terms that do not cancel in standard MCMC algorithms. We propose using several deterministic approximations for the unknown terms, and investigate their interaction with sampling algorithms. We then develop novel exact-sampling-based MCMC methods, the Exchange Algorithm and Latent Histories. For the first time these algorithms do not require separate approximation before sampling begins. Moreover, the Exchange Algorithm outperforms the only alternative sampling algorithm for doubly intractable distributions.
APA, Harvard, Vancouver, ISO, and other styles
9

Graham, Matthew McKenzie. "Auxiliary variable Markov chain Monte Carlo methods." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/28962.

Full text
Abstract:
Markov chain Monte Carlo (MCMC) methods are a widely applicable class of algorithms for estimating integrals in statistical inference problems. A common approach in MCMC methods is to introduce additional auxiliary variables into the Markov chain state and perform transitions in the joint space of target and auxiliary variables. In this thesis we consider novel methods for using auxiliary variables within MCMC methods to allow approximate inference in otherwise intractable models and to improve sampling performance in models exhibiting challenging properties such as multimodality. We first consider the pseudo-marginal framework. This extends the Metropolis–Hastings algorithm to cases where we only have access to an unbiased estimator of the density of target distribution. The resulting chains can sometimes show ‘sticking’ behaviour where long series of proposed updates are rejected. Further the algorithms can be difficult to tune and it is not immediately clear how to generalise the approach to alternative transition operators. We show that if the auxiliary variables used in the density estimator are included in the chain state it is possible to use new transition operators such as those based on slice-sampling algorithms within a pseudo-marginal setting. This auxiliary pseudo-marginal approach leads to easier to tune methods and is often able to improve sampling efficiency over existing approaches. As a second contribution we consider inference in probabilistic models defined via a generative process with the probability density of the outputs of this process only implicitly defined. The approximate Bayesian computation (ABC) framework allows inference in such models when conditioning on the values of observed model variables by making the approximation that generated observed variables are ‘close’ rather than exactly equal to observed data. Although making the inference problem more tractable, the approximation error introduced in ABC methods can be difficult to quantify and standard algorithms tend to perform poorly when conditioning on high dimensional observations. This often requires further approximation by reducing the observations to lower dimensional summary statistics. We show how including all of the random variables used in generating model outputs as auxiliary variables in a Markov chain state can allow the use of more efficient and robust MCMC methods such as slice sampling and Hamiltonian Monte Carlo (HMC) within an ABC framework. In some cases this can allow inference when conditioning on the full set of observed values when standard ABC methods require reduction to lower dimensional summaries for tractability. Further we introduce a novel constrained HMC method for performing inference in a restricted class of differentiable generative models which allows conditioning the generated observed variables to be arbitrarily close to observed data while maintaining computational tractability. As a final topicwe consider the use of an auxiliary temperature variable in MCMC methods to improve exploration of multimodal target densities and allow estimation of normalising constants. Existing approaches such as simulated tempering and annealed importance sampling use temperature variables which take on only a discrete set of values. The performance of these methods can be sensitive to the number and spacing of the temperature values used, and the discrete nature of the temperature variable prevents the use of gradient-based methods such as HMC to update the temperature alongside the target variables. We introduce new MCMC methods which instead use a continuous temperature variable. This both removes the need to tune the choice of discrete temperature values and allows the temperature variable to be updated jointly with the target variables within a HMC method.
APA, Harvard, Vancouver, ISO, and other styles
10

Xu, Jason Qian. "Markov Chain Monte Carlo and Non-Reversible Methods." Thesis, The University of Arizona, 2012. http://hdl.handle.net/10150/244823.

Full text
Abstract:
The bulk of Markov chain Monte Carlo applications make use of reversible chains, relying on the Metropolis-Hastings algorithm or similar methods. While reversible chains have the advantage of being relatively easy to analyze, it has been shown that non-reversible chains may outperform them in various scenarios. Neal proposes an algorithm that transforms a general reversible chain into a non-reversible chain with a construction that does not increase the asymptotic variance. These modified chains work to avoid diffusive backtracking behavior which causes Markov chains to be trapped in one position for too long. In this paper, we provide an introduction to MCMC, and discuss the Metropolis algorithm and Neal’s algorithm. We introduce a decaying memory algorithm inspired by Neal’s idea, and then analyze and compare the performance of these chains on several examples.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Gaussian; Markov chain Monte Carlo methods"

1

Liang, Faming, Chuanhai Liu, and Raymond J. Carroll. Advanced Markov Chain Monte Carlo Methods. Chichester, UK: John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470669723.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Joseph, Anosh. Markov Chain Monte Carlo Methods in Quantum Field Theories. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46044-0.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Liang, F. Advanced Markov chain Monte Carlo methods: Learning from past samples. Hoboken, NJ: Wiley, 2010.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Winkler, Gerhard. Image Analysis, Random Fields and Markov Chain Monte Carlo Methods. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55760-6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Neal, Radford M. Markov chain Monte Carlo methods based on "slicing" the density function. Toronto: University of Toronto, Dept. of Statistics, 1997.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Gerhard, Winkler. Image analysis, random fields and Markov chain Monte Carlo methods: A mathematical introduction. 2nd ed. Berlin: Springer, 2003.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

1946-, Winkler Gerhard, ed. Image analysis, random fields and Markov chain Monte Carlo methods: A mathematical introduction. 2nd ed. Berlin: Springer, 2003.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Cheng, Russell. Finite Mixture Examples; MAPIS Details. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0018.

Full text
Abstract:
Two detailed numerical examples are given in this chapter illustrating and comparing mainly the reversible jump Markov chain Monte Carlo (RJMCMC) and the maximum a posteriori/importance sampling (MAPIS) methods. The numerical examples are the well-known galaxy data set with sample size 82, and the Hidalgo stamp issues thickness data with sample size 485. A comparison is made of the estimates obtained by the RJMCMC and MAPIS methods for (i) the posterior k-distribution of the number of components, k, (ii) the predictive finite mixture distribution itself, and (iii) the posterior distributions of the component parameters and weights. The estimates obtained by MAPIS are shown to be more satisfactory and meaningful. Details are given of the practical implementation of MAPIS for five non-normal mixture models, namely: the extreme value, gamma, inverse Gaussian, lognormal, and Weibull. Mathematical details are also given of the acceptance-rejection importance sampling used in MAPIS.
APA, Harvard, Vancouver, ISO, and other styles
9

Carroll, Raymond, Faming Liang, and Chuanhai Liu. Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples. Wiley & Sons, Incorporated, John, 2011.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Carroll, Raymond, Faming Liang, and Chuanhai Liu. Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples. Wiley & Sons, Incorporated, John, 2010.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Gaussian; Markov chain Monte Carlo methods"

1

Barbu, Adrian, and Song-Chun Zhu. "Markov Chain Monte Carlo: The Basics." In Monte Carlo Methods, 49–70. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-2971-5_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Barbu, Adrian, and Song-Chun Zhu. "Data Driven Markov Chain Monte Carlo." In Monte Carlo Methods, 211–80. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-2971-5_8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Rizzo, Maria L. "Markov Chain Monte Carlo Methods." In Statistical Computing with R, 297–336. Second edition. | Boca Raton : CRC Press, Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429192760-11.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Lange, Kenneth. "Markov Chain Monte Carlo Methods." In Mathematical and Statistical Methods for Genetic Analysis, 142–63. New York, NY: Springer New York, 1997. http://dx.doi.org/10.1007/978-1-4757-2739-5_9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Asmussen, Søren, and Peter W. Glynn. "Markov Chain Monte Carlo Methods." In Stochastic Modelling and Applied Probability, 350–80. New York, NY: Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-69033-9_13.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Chib, Siddhartha. "Markov Chain Monte Carlo Methods." In The New Palgrave Dictionary of Economics, 1–11. London: Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_2042-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Albert, Jim. "Markov Chain Monte Carlo Methods." In Bayesian Computation with R, 117–52. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-92298-0_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Hörmann, Wolfgang, Josef Leydold, and Gerhard Derflinger. "Markov Chain Monte Carlo Methods." In Automatic Nonuniform Random Variate Generation, 363–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-05946-3_14.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Ó Ruanaidh, Joseph J. K., and William J. Fitzgerald. "Markov Chain Monte Carlo Methods." In Numerical Bayesian Methods Applied to Signal Processing, 69–95. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-0717-7_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Robert, Christian P., and Sylvia Richardson. "Markov Chain Monte Carlo Methods." In Discretization and MCMC Convergence Assessment, 1–25. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1716-9_1.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Gaussian; Markov chain Monte Carlo methods"

1

Pandita, Piyush, Jesper Kristensen, and Liping Wang. "Towards Scalable Gaussian Process Modeling." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97010.

Full text
Abstract:
Abstract Numerous engineering problems of interest to the industry are often characterized by expensive black-box objective function evaluations. These objective functions could be physical experiments or computer simulations. Obtaining a comprehensive idea of the problem and/or performing subsequent optimizations generally requires hundreds of thousands of evaluations of the objective function which is most often a practically unachievable task. Gaussian Process (GP) surrogate modeling replaces the expensive function with a cheap-to-evaluate data-driven probabilistic model. While the GP does not assume a functional form of the problem, it is defined by a set of parameters, called hyper-parameters, that need to be learned from the data. The hyperparameters define the characteristics of the objective function, such as smoothness, magnitude, periodicity, etc. Accurately estimating these hyperparameters is a key ingredient in developing a reliable and generalizable surrogate model. Markov chain Monte Carlo (MCMC) is a ubiquitously used Bayesian method to estimate these hyperparameters. At GEs Global Research Center, a customized industry-strength Bayesian hybrid modeling framework utilizing the GP, called GEBHM, has been employed and validated over many years. GEBHM is very effective on problems of small and medium size, typically less than 1000 training points. However, the GP does not scale well in time with a growing dataset and problem dimensionality which can be a major impediment in such problems. For some challenging industry applications, the predictive capability of the GP is required but each second during the training of the GP costs thousands of dollars. In this work, we apply a scalable MCMC-based methodology enabling the modeling of large-scale industry problems. Towards this, we extend and implement in GEBHM an Adaptive Sequential Monte Carlo (ASMC) methodology for training the GP. This implementation saves computational time (especially for large-scale problems) while not sacrificing predictability over the current MCMC implementation. We demonstrate the effectiveness and accuracy of GEBHM with ASMC on four mathematical problems and on two challenging industry applications of varying complexity.
APA, Harvard, Vancouver, ISO, and other styles
2

Khalil, Mohammad, Abhijit Sarkar, and Dominique Poirel. "Application of Bayesian Inference to the Flutter Margin Method: New Developments." In ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting collocated with 8th International Conference on Nanochannels, Microchannels, and Minichannels. ASMEDC, 2010. http://dx.doi.org/10.1115/fedsm-icnmm2010-30041.

Full text
Abstract:
Zimmerman and Weissenburger flutter margin method is extended to account for modal parameter uncertainties by applying a Bayesian estimation technique to obtain the probability distribution function of the flutter speed. In previous work, a least-squares estimation technique was applied to obtain the posterior pdf of the flutter speed. The limitation of this technique is the assumption that the flutter margin at each airspeed is strictly Gaussian. In this paper, the joint distribution of the modal parameters (and consequently the flutter margin) is obtained from preflutter measured system responses using a full Bayesian analysis utilizing Markov Chain Monte Carlo sampling technique. The flutter margin pdfs are then utilized to obtain the posterior probability density function of the flutter speed. Results are presented for a two-degrees-of-freedom numerical model, for which the true flutter speed is known.
APA, Harvard, Vancouver, ISO, and other styles
3

Gang, Jinhyuk, Jooho Choi, Bonghee Lee, and Jinwon Joo. "Material Parameter Identification of Viscoplastic Model for Solder Alloy in Electronics Package Using Bayesian Calibration." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28603.

Full text
Abstract:
In this study, a method of computer model calibration is applied to quantify the uncertainties arising in the material characterization of the solder joint in the microelectronics package subject to a thermal cycle. In this study, all uncertainties are addressed by using a Bayesian calibration approach. A special specimen that characterizes the solder property due to the shear deformation is prepared, from which the Moire´ fringe is measured by running a thermal cycle. Viscoplastic finite element analysis procedure is constructed for the specimen based on the Anand model. Gaussian process model known as Kriging is employed to approximate the original finite element analysis (FEA) model. Posterior distribution for the unknown Anand parameters is formulated from the likelihood function for joint full-field displacements of computation and experiment. Markov Chain Monte Carlo (MCMC) method is employed to simulate posterior distribution. As a result, the displacements are predicted in the form of confidence interval. The results show that the proposed approach can be a useful tool in the estimation of the unknown material parameters in a probabilistic manner by effectively accounting for the uncertainties due to the experimental and computational models.
APA, Harvard, Vancouver, ISO, and other styles
4

Runnalls, A. "Monte Carlo Markov chain methods for tracking." In IEE Colloquium on `Algorithms for Target Tracking'. IEE, 1995. http://dx.doi.org/10.1049/ic:19950668.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Wang, Zheng, Cong Ling, and Guillaume Hanrot. "Markov chain Monte Carlo algorithms for lattice Gaussian sampling." In 2014 IEEE International Symposium on Information Theory (ISIT). IEEE, 2014. http://dx.doi.org/10.1109/isit.2014.6875081.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Wadsley, Andrew W. "Markov Chain Monte Carlo Methods for Reserves Estimation." In International Petroleum Technology Conference. International Petroleum Technology Conference, 2005. http://dx.doi.org/10.2523/10065-ms.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Wadsley, Andrew W. "Markov Chain Monte Carlo Methods for Reserves Estimation." In International Petroleum Technology Conference. International Petroleum Technology Conference, 2005. http://dx.doi.org/10.2523/iptc-10065-ms.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Somersalo, Erkki, Jari P. Kaipio, Marko J. Vauhkonen, D. Baroudi, and S. Jaervenpaeae. "Impedance imaging and Markov chain Monte Carlo methods." In Optical Science, Engineering and Instrumentation '97, edited by Randall L. Barbour, Mark J. Carvlin, and Michael A. Fiddy. SPIE, 1997. http://dx.doi.org/10.1117/12.279723.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Gerencser, L., S. D. Hill, Z. Vago, and Z. Vincze. "Discrete optimization, SPSA and Markov chain Monte Carlo methods." In Proceedings of the 2004 American Control Conference. IEEE, 2004. http://dx.doi.org/10.23919/acc.2004.1384507.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Khalil, Mohammad, Abhijit Sarkar, and Dominique Poirel. "Parameter Estimation of a Fluttering Aeroelastic System in the Transitional Reynolds Number Regime." In ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting collocated with 8th International Conference on Nanochannels, Microchannels, and Minichannels. ASMEDC, 2010. http://dx.doi.org/10.1115/fedsm-icnmm2010-30047.

Full text
Abstract:
We report the parameter estimation results of a self-sustaining aeroelastic oscillator. The system is composed of a rigid wing that is elastically mounted on a rig, which in turn is fixed in a wind tunnel. For certain flow conditions, in particular dictated by the Reynolds number in the transitional regime, the wing extracts energy from the flow leading to a stable limit cycle oscillation. The basic physical mechanism at the origin of the oscillations is laminar boundary layer separation, which leads to negative aerodynamic damping. An empirical model of the aeroelastic system is proposed in the form of a generalized Duffing-van der Pol oscillator, whereby the linear and nonlinear aeroelastic terms are unknowns to be estimated. The model (input) noise process accounting for the amplitude modulation observed from experiments will also be estimated. We apply a Bayesian inference based batch data assimilation method in tackling this strongly nonlinear and non-Gaussian model. In particular, Markov Chain Monte Carlo sampling technique is used to generate samples from the joint distribution of the unknown parameters given noisy measurement data. The extended Kalman filter is utilized to obtain the conditional distribution of the model state given the noisy measurements. The parameter estimates for a third order generalized Duffing-van der Pol oscillator are obtained and marginal and joint probability density functions for the parameters will be presented for both a numerical model and a rigid wing that is elastically mounted on a rig in a wind tunnel.
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Gaussian; Markov chain Monte Carlo methods"

1

Doss, Hani. Statistical Inference for Coherent Systems from Partial Information and Markov Chain Monte Carlo Methods. Fort Belvoir, VA: Defense Technical Information Center, January 1996. http://dx.doi.org/10.21236/ada305676.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Doss, Hani. Studies in Reliability Theory and Survival Analysis and in Markov Chain Monte Carlo Methods. Fort Belvoir, VA: Defense Technical Information Center, September 1998. http://dx.doi.org/10.21236/ada367895.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Doss, Hani. Studies in Reliability Theory and Survival Analysis and in Markov Chain Monte Carlo Methods. Fort Belvoir, VA: Defense Technical Information Center, December 1998. http://dx.doi.org/10.21236/ada379998.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Glaser, R., G. Johannesson, S. Sengupta, B. Kosovic, S. Carle, G. Franz, R. Aines, et al. Stochastic Engine Final Report: Applying Markov Chain Monte Carlo Methods with Importance Sampling to Large-Scale Data-Driven Simulation. Office of Scientific and Technical Information (OSTI), March 2004. http://dx.doi.org/10.2172/15009813.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Gaussian; Markov chain Monte Carlo methods' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography