Rozprawy doktorskie: „Bayesian”

1

Kennedy, Marc. "Bayesian quadrature and Bayesian rescaling". Thesis, University of Nottingham, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.319655.

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Nappa, Dario. "Bayesian classification using Bayesian additive and regression trees". Ann Arbor, Mich. : ProQuest, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3336814.

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Thesis (Ph.D. in Statistical Sciences)--S.M.U.
Title from PDF title page (viewed Mar. 16, 2009). Source: Dissertation Abstracts International, Volume: 69-12, Section: B, page: . Adviser: Xinlei Wang. Includes bibliographical references.

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Yu, Qingzhao. "Bayesian synthesis". Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155324080.

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Duggan, John Palfrey Thomas R. Palfrey Thomas R. "Bayesian implementation /". Diss., Pasadena, Calif. : California Institute of Technology, 1995. http://resolver.caltech.edu/CaltechETD:etd-09182007-084408.

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Filho, Paulo Cilas Marques. "Análise bayesiana de densidades aleatórias simples". Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-25052012-184549/.

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Definimos, a partir de uma partição de um intervalo limitado da reta real formada por subintervalos, uma distribuição a priori sobre uma classe de densidades em relação à medida de Lebesgue construindo uma densidade aleatória cujas realizações são funções simples não negativas que assumem um valor constante em cada subintervalo da partição e possuem integral unitária. Utilizamos tais densidades aleatórias simples na análise bayesiana de um conjunto de observáveis absolutamente contínuos e provamos que a distribuição a priori é fechada sob amostragem. Exploramos as distribuições a priori e a posteriori via simulações estocásticas e obtemos soluções bayesianas para o problema de estimação de densidade. Os resultados das simulações exibem o comportamento assintótico da distribuição a posteriori quando crescemos o tamanho das amostras dos dados analisados. Quando a partição não é conhecida a priori, propomos um critério de escolha a partir da informação contida na amostra. Apesar de a esperança de uma densidade aleatória simples ser sempre uma densidade descontínua, obtemos estimativas suaves resolvendo um problema de decisão em que os estados da natureza são realizações da densidade aleatória simples e as ações são densidades suaves de uma classe adequada.
We define, from a known partition in subintervals of a bounded interval of the real line, a prior distribution over a class of densities with respect to Lebesgue measure constructing a random density whose realizations are nonnegative simple functions that integrate to one and have a constant value on each subinterval of the partition. These simple random densities are used in the Bayesian analysis of a set of absolutely continuous observables and the prior distribution is proved to be closed under sampling. We explore the prior and posterior distributions through stochastic simulations and find Bayesian solutions to the problem of density estimation. Simulations results show the asymptotic behavior of the posterior distribution as we increase the size of the analyzed data samples. When the partition is unknown, we propose a choice criterion based on the information contained in the sample. In spite of the fact that the expectation of a simple random density is always a discontinuous density, we get smooth estimates solving a decision problem where the states of nature are realizations of the simple random density and the actions are smooth densities of a suitable class.

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Cheng, Dunlei Stamey James D. "Topics in Bayesian sample size determination and Bayesian model selection". Waco, Tex. : Baylor University, 2007. http://hdl.handle.net/2104/5039.

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Tseng, Shih-Hsien. "Bayesian and Semi-Bayesian regression applied to manufacturing wooden products". The Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=osu1199240473.

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Pramanik, Santanu. "The Bayesian and approximate Bayesian methods in small area estimation". College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8856.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2008.
Thesis research directed by: Joint Program in Survey Methodology. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

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9

Næss, Arild Brandrud. "Bayesian Text Categorization". Thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9665.

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Natural language processing is an interdisciplinary field of research which studies the problems and possibilities of automated generation and understanding of natural human languages. Text categorization is a central subfield of natural language processing. Automatically assigning categories to digital texts has a wide range of applications in todays information societyfrom filtering spam to creating web hierarchies and digital newspaper archives. It is a discipline that lends itself more naturally to machine learning than to knowledge engineering; statistical approaches to text categorization are therefore a promising field of inquiry. We provide a survey of the state of the art in text categorization, presenting the most widespread methods in use, and placing particular emphasis on support vector machinesan optimization algorithm that has emerged as the benchmark method in text categorization in the past ten years. We then turn our attention to Bayesian logistic regression, a fairly new, and largely unstudied method in text categorization. We see how this method has certain similarities to the support vector machine method, but also differs from it in crucial respects. Notably, Bayesian logistic regression provides us with a statistical framework. It can be claimed to be more modular, in the sense that it is more open to modifications and supplementations by other statistical methods; whereas the support vector machine method remains more of a black box. We present results of thorough testing of the BBR toolkit for Bayesian logistic regression on three separate data sets. We demonstrate which of BBRs parameters are of importance; and we show that its results compare favorably to those of the SVMli ght toolkit for support vector machines. We also present two extensions to the BBR toolkit. One attempts to incorporate domain knowledge by way of the prior probability distributions of single words; the other tries to make use of uncategorized documents to boost learning accuracy.

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10

Maezawa, Akira. "Bayesian Music Alignment". 京都大学 (Kyoto University), 2015. http://hdl.handle.net/2433/199430.

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11

Horsch, Michael C. "Dynamic Bayesian networks". Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/28909.

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Given the complexity of the domains for which we would like to use computers as reasoning engines, an automated reasoning process will often be required to perform under some state of uncertainty. Probability provides a normative theory with which uncertainty can be modelled. Without assumptions of independence from the domain, naive computations of probability are intractible. If probability theory is to be used effectively in AI applications, the independence assumptions from the domain should be represented explicitly, and used to greatest possible advantage. One such representation is a class of mathematical structures called Bayesian networks. This thesis presents a framework for dynamically constructing and evaluating Bayesian networks. In particular, this thesis investigates the issue of representing probabilistic knowledge which has been abstracted from particular individuals to which this knowledge may apply, resulting in a simple representation language. This language makes the independence assumptions for a domain explicit. A simple procedure is provided for building networks from knowledge expressed in this language. The mapping between the knowledge base and network created is precisely defined, so that the network always represents a consistent probability distribution. Finally, this thesis investigates the issue of modifying the network after some evaluation has taken place, and several techniques for correcting the state of the resulting model are derived.
Science, Faculty of
Computer Science, Department of
Graduate

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Koepke, Hoyt Adam. "Bayesian cluster validation". Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/1496.

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We propose a novel framework based on Bayesian principles for validating clusterings and present efficient algorithms for use with centroid or exemplar based clustering solutions. Our framework treats the data as fixed and introduces perturbations into the clustering procedure. In our algorithms, we scale the distances between points by a random variable whose distribution is tuned against a baseline null dataset. The random variable is integrated out, yielding a soft assignment matrix that gives the behavior under perturbation of the points relative to each of the clusters. From this soft assignment matrix, we are able to visualize inter-cluster behavior, rank clusters, and give a scalar index of the the clustering stability. In a large test on synthetic data, our method matches or outperforms other leading methods at predicting the correct number of clusters. We also present a theoretical analysis of our approach, which suggests that it is useful for high dimensional data.

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13

Hospedales, Timothy. "Bayesian multisensory perception". Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/2156.

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A key goal for humans and artificial intelligence systems is to develop an accurate and unified picture of the outside world based on the data from any sense(s) that may be available. The availability of multiple senses presents the perceptual system with new opportunities to fulfil this goal, but exploiting these opportunities first requires the solution of two related tasks. The first is how to make the best use of any redundant information from the sensors to produce the most accurate percept of the state of the world. The second is how to interpret the relationship between observations in each modality; for example, the correspondence problem of whether or not they originate from the same source. This thesis investigates these questions using ideal Bayesian observers as the underlying theoretical approach. In particular, the latter correspondence task is treated as a problem of Bayesian model selection or structure inference in Bayesian networks. This approach provides a unified and principled way of representing and understanding the perceptual problems faced by humans and machines and their commonality. In the domain of machine intelligence, we exploit the developed theory for practical benefit, developing a model to represent audio-visual correlations. Unsupervised learning in this model provides automatic calibration and user appearance learning, without human intervention. Inference in the model involves explicit reasoning about the association between latent sources and observations. This provides audio-visual tracking through occlusion with improved accuracy compared to standard techniques. It also provides detection, verification and speech segmentation, ultimately allowing the machine to understand ``who said what, where?'' in multi-party conversations. In the domain of human neuroscience, we show how a variety of recent results in multimodal perception can be understood as the consequence of probabilistic reasoning about the causal structure of multimodal observations. We show this for a localisation task in audio-visual psychophysics, which is very similar to the task solved by our machine learning system. We also use the same theory to understand results from experiments in the completely different paradigm of oddity detection using visual and haptic modalities. These results begin to suggest that the human perceptual system performs -- or at least approximates -- sophisticated probabilistic reasoning about the causal structure of observations under the hood.

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14

Abrams, Keith Rowland. "Bayesian survival analysis". Thesis, University of Liverpool, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316744.

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In cancer research the efficacy of a new treatment is often assessed by means of a clinical trial. In such trials the outcome measure of interest is usually time to death from entry into the study. The time to intermediate events may also be of interest, for example time to the spread of the disease to other organs (metastases). Thus, cancer clinical trials can be seen to generate multi-state data, in which patients may be in anyone of a finite number of states at a particular time. The classical analysis of data from cancer clinical trials uses a survival regression model. This type of model allows for the fact that patients in the trial will have been observed for different lengths of time and for some patients the time to the event of interest will not be observed (censored). The regression structure means that a measure of treatment effect can be obtained after allowing for other important factors. Clinical trials are not conducted in isolation, but are part of an on-going learning process. In order to assess the current weight of evidence for the use of a particular treatment a Bayesian approach is necessary. Such an approach allows for the formal inclusion of prior information, either in the form of clinical expertise or the results from previous studies, into the statistical analysis. An initial Bayesian analysis, for a single non-recurrent event, can be performed using non-temporal models that consider the occurrence of events up to a specific time from entry into the study. Although these models are conceptually simple, they do not explicitly allow for censoring or covariates. In order to address both of these deficiencies a Bayesian fully parametric multiplicative intensity regression model is developed. The extra complexity of this model means that approximate integration techniques are required. Asymptotic Laplace approximations and the more computer intensive Gauss-Hermite quadrature are shown to perform well and yield virtually identical results. By adopting counting process notation the multiplicative intensity model is extended to the multi-state scenario quite easily. These models are used in the analysis of a cancer clinical trial to assess the efficacy of neutron therapy compared to standard photon therapy for patients with cancer of the pelvic region. In this trial there is prior information both in the form of clinical prior beliefs and results from previous studies. The usefulness of multi-state models is also demonstrated in the analysis of a pilot quality of life study. Bayesian multi-state models are shown to provide a coherent framework for the analysis of clinical studies, both interventionist and observational, yielding clinically meaningful summaries about the current state of knowledge concerning the disease/treatment process.

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Jeng, Ji-Tian. "Bayesian aggregative games". Thesis, Keele University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.417848.

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This thesis considers a model of Coumot competition where firms have incomplete information about their rivals' costs. The equilibrium concept we use is that of Bayesian- Nash equilibrium. Based on the recognition of the "aggregative structure" within Coumot competition in which each finn's payoff is determined by her own strategy choice and the unweighted sum of all firms' strategy choices, we are able to characterise equilibria in a very simple way. We show that when we consider not the best response but the strategy consistent with a Nash equilibrium in which the aggregate strategy of all players take same value (which is given by what we call the replacement function), then Nash equilibria correspond to fixed points of the aggregate replacement function whose properties we can certainly obtain without need for restricting our attention to symmetric games or games in which there are just 2 players. We develop sufficient conditions under which there is a unique equilibrium. The approach facilitates the analyses of competitive limit and comparative statics, since the characterisation of Bayesian-Nash equilibria can be shown on a two-dimensional space. We also examine two applications, which include information sharing and the relative efficiency of an ad valorem tax scheme as opposed to a specific tax scheme.

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Edgington, Padraic D. "Modular Bayesian filters". Thesis, University of Louisiana at Lafayette, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3712276.

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In this dissertation, I introduce modularization as a means of efficiently solving problems represented by dynamic Bayesian networks and study the properties and effects of modularization relative to traditional solutions. Modularizing a Bayesian filter allows its results to be calculated faster than a traditional Bayesian filter. Traditional Bayesian filters can have issues when large problems must be solved within a short period of time. Modularization addresses this issue by dividing the full problem into a set of smaller problems that can then be solved with separate Bayesian filters. Since the time complexity of Bayesian filters is greater than linear, solving several smaller problems is cheaper than solving a single large problem. The cost of reassembling the results from the smaller problems is comparable to the cost of the smaller problems. This document introduces the concept of both exact and approximate modular Bayesian filters and describes how to design each of the elements of a modular Bayesian filters. These concepts are clarified by using a series of examples from the realm of vehicle state estimation and include the results of each stage of the algorithm creation in a simulated environment. A final section shows the implementation of a modular Bayesian filter in a real-world problem tasked with addressing the problem of vehicle state estimation in the face of transitory sensor failure. This section also includes all of the attending algorithms that allow the problem to be solved accurately and in real-time.

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Rhodes, Darren. "Bayesian time perception". Thesis, University of Birmingham, 2016. http://etheses.bham.ac.uk//id/eprint/6608/.

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Time is an elemental dimension of human perception, cognition and action. Innumerable studies have investigated the perception of time over the last 100 years, but the computational basis for the processing of temporal information remains unknown. This thesis aims to understand the mechanisms underlying the perceived timing of stimuli. We propose a novel Bayesian model of when stimuli are perceived that is consistent with the predictive coding framework – such a perspective to how the brain deals with temporal information forms the core of this thesis. We theorize that that the brain takes prior expectations about when a stimulus might occur in the future (prior distribution) and combines it with current sensory evidence (likelihood function) in order to generate a percept of perceived timing (posterior distribution). In Chapters 2-4, we use human psychophysics to show that the brain may bias perception such that slightly irregularly timed stimuli as reported as more regular. In Chapter 3, we show how an environment of irregularity can cause regularly timed sequences to be perceived as irregular whilst Chapter 4 shows how changes in the reliability of a signal can cause an increased attraction towards expectation.

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Campbell, Trevor D. J. (Trevor David Jan). "Truncated Bayesian nonparametrics". Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/107047.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 167-175).
Many datasets can be thought of as expressing a collection of underlying traits with unknown cardinality. Moreover, these datasets are often persistently growing, and we expect the number of expressed traits to likewise increase over time. Priors from Bayesian nonparametrics are well-suited to this modeling challenge: they generate a countably infinite number of underlying traits, which allows the number of expressed traits to both be random and to grow with the dataset size. We also require corresponding streaming, distributed inference algorithms that handle persistently growing datasets without slowing down over time. However, a key ingredient in streaming, distributed inference-an explicit representation of the latent variables used to statistically decouple the data-is not available for nonparametric priors, as we cannot simulate or store infinitely many random variables in practice. One approach is to approximate the nonparametric prior by developing a sequential representation-such that the traits are generated by a sequence of finite-dimensional distributions-and subsequently truncating it at some finite level, thus allowing explicit representation. However, truncated sequential representations have been developed only for a small number of priors in Bayesian nonparametrics, and the order they impose on the traits creates identifiability issues in the streaming, distributed setting. This thesis provides a comprehensive theoretical treatment of sequential representations and truncation in Bayesian nonparametrics. It details three sequential representations of a large class of nonparametric priors, and analyzes their truncation error and computational complexity. The results generalize and improve upon those existing in the literature. Next, the truncated explicit representations are used to develop the first streaming, distributed, asynchronous inference procedures for models from Bayesian nonparametrics. The combinatorial issues associated with trait identifiability in such models are resolved via a novel matching optimization. The resulting algorithms are fast, learning rate-free, and truncation-free. Taken together, these contributions provide the practitioner with the means to (1) develop multiple finite approximations for a given nonparametric prior; (2) determine which is the best for their application; and (3) use that approximation in the development of efficient streaming, distributed, asynchronous inference algorithms.
by Trevor David Jan Campbell.
Ph. D.

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19

Buck, Caitlin E. "Towards Bayesian archaeology". Thesis, University of Nottingham, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.385208.

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Barillec, Remi Louis. "Bayesian data assimilation". Thesis, Aston University, 2008. http://publications.aston.ac.uk/15276/.

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This thesis addresses data assimilation, which typically refers to the estimation of the state of a physical system given a model and observations, and its application to short-term precipitation forecasting. A general introduction to data assimilation is given, both from a deterministic and stochastic point of view. Data assimilation algorithms are reviewed, in the static case (when no dynamics are involved), then in the dynamic case. A double experiment on two non-linear models, the Lorenz 63 and the Lorenz 96 models, is run and the comparative performance of the methods is discussed in terms of quality of the assimilation, robustness in the non-linear regime and computational time.

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Keim, Michelle. "Bayesian information retrieval /". Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/8937.

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Bendtsen, Marcus. "Gated Bayesian Networks". Doctoral thesis, Linköpings universitet, Databas och informationsteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-136761.

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Bayesian networks have grown to become a dominant type of model within the domain of probabilistic graphical models. Not only do they empower users with a graphical means for describing the relationships among random variables, but they also allow for (potentially) fewer parameters to estimate, and enable more efficient inference. The random variables and the relationships among them decide the structure of the directed acyclic graph that represents the Bayesian network. It is the stasis over time of these two components that we question in this thesis. By introducing a new type of probabilistic graphical model, which we call gated Bayesian networks, we allow for the variables that we include in our model, and the relationships among them, to change overtime. We introduce algorithms that can learn gated Bayesian networks that use different variables at different times, required due to the process which we are modelling going through distinct phases. We evaluate the efficacy of these algorithms within the domain of algorithmic trading, showing how the learnt gated Bayesian networks can improve upon a passive approach to trading. We also introduce algorithms that detect changes in the relationships among the random variables, allowing us to create a model that consists of several Bayesian networks, thereby revealing changes and the structure by which these changes occur. The resulting models can be used to detect the currently most appropriate Bayesian network, and we show their use in real-world examples from both the domain of sports analytics and finance.

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Jones, Emma. "Practical Bayesian dendrochronology". Thesis, University of Sheffield, 2013. http://etheses.whiterose.ac.uk/4130/.

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Mao, Weijie. "Bayesian multivariate predictions". Diss., University of Iowa, 2010. https://ir.uiowa.edu/etd/853.

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This work offers two strategies to raise the prediction accuracy of Vector Autoregressive (VAR) Models. The first strategy is to improve the Minnesota prior, which is frequently used for Bayesian VAR models. The improvement is achieved in two ways. First, the variance-covariance matrix of regression disturbances is treated as unknown and random to incorporate parameter uncertainty. Second, the prior variance-covariance matrix of regression coefficients is constructed as a function of the variance-covariance matrix of disturbances, in order to account for dependencies between different equations. Since different prior specifications unavoidably lead to different models, and forecasting capability of any such model is often limited, the second strategy is to build an optimal prediction pool of models by using the conventional log predictive score function. The effectiveness of the proposed strategies is examined for one-step-ahead, multi-4-step-ahead, and single-4-step-ahead predictions through two exercises. One exercise is predicting national output, inflation, and interest rate in the United States, and the other is predicting state tax revenue and personal income in Iowa. The empirical results indicate that a properly selected prior can improve the prediction performance of a BVAR model, and that a real-time optimal prediction pool can outperform a single best constituent model alone.

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LEGRAMANTI, SIRIO. "Bayesian dimensionality reduction". Doctoral thesis, Università Bocconi, 2021. http://hdl.handle.net/11565/4035711.

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No abstract available
We are currently witnessing an explosion in the amount of available data. Such growth involves not only the number of data points but also their dimensionality. This poses new challenges to statistical modeling and computations, thus making dimensionality reduction more central than ever. In the present thesis, we provide methodological, computational and theoretical advancements in Bayesian dimensionality reduction via novel structured priors. Namely, we develop a new increasing shrinkage prior and illustrate how it can be employed to discard redundant dimensions in Gaussian factor models. In order to make it usable for larger datasets, we also investigate variational methods for posterior inference under this proposed prior. Beyond traditional models and parameter spaces, we also provide a different take on dimensionality reduction, focusing on community detection in networks. For this purpose, we define a general class of Bayesian nonparametric priors that encompasses existing stochastic block models as special cases and includes promising unexplored options. Our Bayesian approach allows for a natural incorporation of node attributes and facilitates uncertainty quantification as well as model selection.

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Ma, Yimin. "Bayesian and empirical Bayesian analysis for the truncation parameter distribution families". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0027/NQ51000.pdf.

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Ma, Yimin. "Bayesian and empirical Bayesian analysis for the truncation parameter distribution families /". *McMaster only, 1998.

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Datta, Sagnik. "Fully bayesian structure learning of bayesian networks and their hypergraph extensions". Thesis, Compiègne, 2016. http://www.theses.fr/2016COMP2283.

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Dans cette thèse, j’aborde le problème important de l’estimation de la structure des réseaux complexes, à l’aide de la classe des modèles stochastiques dits réseaux Bayésiens. Les réseaux Bayésiens permettent de représenter l’ensemble des relations d’indépendance conditionnelle. L’apprentissage statistique de la structure de ces réseaux complexes par les réseaux Bayésiens peut révéler la structure causale sous-jacente. Il peut également servir pour la prédiction de quantités qui sont difficiles, coûteuses, ou non éthiques comme par exemple le calcul de la probabilité de survenance d’un cancer à partir de l’observation de quantités annexes, plus faciles à obtenir. Les contributions de ma thèse consistent en : (A) un logiciel développé en langage C pour l’apprentissage de la structure des réseaux bayésiens; (B) l’introduction d’un nouveau "jumping kernel" dans l’algorithme de "Metropolis-Hasting" pour un échantillonnage rapide de réseaux; (C) l’extension de la notion de réseaux Bayésiens aux structures incluant des boucles et (D) un logiciel spécifique pour l’apprentissage des structures cycliques. Notre principal objectif est l’apprentissage statistique de la structure de réseaux complexes représentée par un graphe et par conséquent notre objet d’intérêt est cette structure graphique. Un graphe est constitué de nœuds et d’arcs. Tous les paramètres apparaissant dans le modèle mathématique et différents de ceux qui caractérisent la structure graphique sont considérés comme des paramètres de nuisance
In this thesis, I address the important problem of the determination of the structure of complex networks, with the widely used class of Bayesian network models as a concrete vehicle of my ideas. The structure of a Bayesian network represents a set of conditional independence relations that hold in the domain. Learning the structure of the Bayesian network model that represents a domain can reveal insights into its underlying causal structure. Moreover, it can also be used for prediction of quantities that are difficult, expensive, or unethical to measure such as the probability of cancer based on other quantities that are easier to obtain. The contributions of this thesis include (A) a software developed in C language for structure learning of Bayesian networks; (B) introduction a new jumping kernel in the Metropolis-Hasting algorithm for faster sampling of networks (C) extending the notion of Bayesian networks to structures involving loops and (D) a software developed specifically to learn cyclic structures. Our primary objective is structure learning and thus the graph structure is our parameter of interest. We intend not to perform estimation of the parameters involved in the mathematical models

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Moreira, Hugo Francisco Vicente. "The effect of heteroscedasticity on bayesian variable selection". Master's thesis, Instituto Superior de Economia e Gestão, 2019. http://hdl.handle.net/10400.5/19769.

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Mestrado em Econometria Aplicada e Previsão
Nesta dissertação estudamos o efeito da heterocedasticidade na seleção bayesiana de variáveis. Através de um estudo de simulação, e utilizando dois conjuntos de dados reais, avaliamos os efeitos de introduzir heteroscedasticidade numa regressão linear, bem como o efeito de transformar dados heterocedásticos em homocedásticos. Analisando as variáveis selecionadas, probabilidades de inclusão e medidas de performance preditiva, concluimos que a seleção bayesiana de variáveis é robusta à heterocedasticidade, mas é possível obter melhor perfomance preditiva se a estrutura de variância dos erros for tomada em conta.
This dissertation aims to study the effect of heteroscedasticity on Bayesian Variable Selection. It employs a simulation study, using two distinct datasets, to evaluate the effects of introducing heteroscedasticity in a linear regression, and whether transforming an heteroscedastic dataset into an homoscedastic one results in any considerable differences. We look at the variables selected, inclusion probabilities and performance measures. We find Bayesian Variable Selection to be robust to heteroscedasticity, although a better predictive performance may be attained if we take the error variance's structure explicitly into account.
info:eu-repo/semantics/publishedVersion

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30

Seeger, Matthias. "Bayesian Gaussian process models : PAC-Bayesian generalisation error bounds and sparse approximations". Thesis, University of Edinburgh, 2003. http://hdl.handle.net/1842/321.

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Non-parametric models and techniques enjoy a growing popularity in the field of machine learning, and among these Bayesian inference for Gaussian process (GP) models has recently received significant attention. We feel that GP priors should be part of the standard toolbox for constructing models relevant to machine learning in the same way as parametric linear models are, and the results in this thesis help to remove some obstacles on the way towards this goal. In the first main chapter, we provide a distribution-free finite sample bound on the difference between generalisation and empirical (training) error for GP classification methods. While the general theorem (the PAC-Bayesian bound) is not new, we give a much simplified and somewhat generalised derivation and point out the underlying core technique (convex duality) explicitly. Furthermore, the application to GP models is novel (to our knowledge). A central feature of this bound is that its quality depends crucially on task knowledge being encoded faithfully in the model and prior distributions, so there is a mutual benefit between a sharp theoretical guarantee and empirically well-established statistical practices. Extensive simulations on real-world classification tasks indicate an impressive tightness of the bound, in spite of the fact that many previous bounds for related kernel machines fail to give non-trivial guarantees in this practically relevant regime. In the second main chapter, sparse approximations are developed to address the problem of the unfavourable scaling of most GP techniques with large training sets. Due to its high importance in practice, this problem has received a lot of attention recently. We demonstrate the tractability and usefulness of simple greedy forward selection with information-theoretic criteria previously used in active learning (or sequential design) and develop generic schemes for automatic model selection with many (hyper)parameters. We suggest two new generic schemes and evaluate some of their variants on large real-world classification and regression tasks. These schemes and their underlying principles (which are clearly stated and analysed) can be applied to obtain sparse approximations for a wide regime of GP models far beyond the special cases we studied here.

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31

Gold, David L. "Bayesian learning in bioinformatics". [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1624.

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32

Buland, Arild. "Bayesian Seismic AVO Inversion". Doctoral thesis, Norwegian University of Science and Technology, Department of Mathematical Sciences, 2002. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-2005.

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Seismic analysis is a key element in successful exploration and production of natural resources. During the last decades, seismic methodology has had a significant progress with respect to both acquisition, processing and analysis. Despite all the new tec hnology, the uncertainty related to seismic analysis is still large, and even worse, the uncertainty is often not systematically assessed.

In this thesis, the uncertainty aspect of seismic amplitude versus offset (AVO) in version is assessed using a Bayesian approach to inversion. The main objective is to estimate elastic material parameters with associated uncertainty from large seismic data sets, but the in versionproblem also includes estimation of seismic wavelets and the noise level. State of the art statistical methodology is applied to attack these current and crucial geophysical problems. The core part of the work is presented in four separate papers written for geophysical journals, constituting Chapter 2 through 5 in this thesis. Each of the papers is self-contained, with exception of the references which are placed in a separate bibliography chapter.

Paper I, II and III: copyright SEG Paper III: copyright EAGE

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33

Yilmaz, Yildiz Elif. "Bayesian Learning Under Nonnormality". Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/3/12605582/index.pdf.

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Naive Bayes classifier and maximum likelihood hypotheses in Bayesian learning are considered when the errors have non-normal distribution. For location and scale parameters, efficient and robust estimators that are obtained by using the modified maximum likelihood estimation (MML) technique are used. In naive Bayes classifier, the error distributions from class to class and from feature to feature are assumed to be non-identical and Generalized Secant Hyperbolic (GSH) and Generalized Logistic (GL) distribution families have been used instead of normal distribution. It is shown that the non-normal naive Bayes classifier obtained in this way classifies the data more accurately than the one based on the normality assumption. Furthermore, the maximum likelihood (ML) hypotheses are obtained under the assumption of non-normality, which also produce better results compared to the conventional ML approach.

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34

Baladandayuthapani, Veerabhadran. "Bayesian methods in bioinformatics". Texas A&M University, 2005. http://hdl.handle.net/1969.1/4856.

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This work is directed towards developing flexible Bayesian statistical methods in the semi- and nonparamteric regression modeling framework with special focus on analyzing data from biological and genetic experiments. This dissertation attempts to solve two such problems in this area. In the first part, we study penalized regression splines (P-splines), which are low-order basis splines with a penalty to avoid under- smoothing. Such P-splines are typically not spatially adaptive, and hence can have trouble when functions are varying rapidly. We model the penalty parameter inherent in the P-spline method as a heteroscedastic regression function. We develop a full Bayesian hierarchical structure to do this and use Markov Chain Monte Carlo tech- niques for drawing random samples from the posterior for inference. We show that the approach achieves very competitive performance as compared to other methods. The second part focuses on modeling DNA microarray data. Microarray technology enables us to monitor the expression levels of thousands of genes simultaneously and hence to obtain a better picture of the interactions between the genes. In order to understand the biological structure underlying these gene interactions, we present a hierarchical nonparametric Bayesian model based on Multivariate Adaptive Regres-sion Splines (MARS) to capture the functional relationship between genes and also between genes and disease status. The novelty of the approach lies in the attempt to capture the complex nonlinear dependencies between the genes which could otherwise be missed by linear approaches. The Bayesian model is flexible enough to identify significant genes of interest as well as model the functional relationships between the genes. The effectiveness of the proposed methodology is illustrated on leukemia and breast cancer datasets.

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35

Otsuka, Takuma. "Bayesian Microphone Array Processing". 京都大学 (Kyoto University), 2014. http://hdl.handle.net/2433/188871.

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Kyoto University (京都大学)
0048
新制・課程博士
博士(情報学)
甲第18412号
情博第527号
新制||情||93(附属図書館)
31270
京都大学大学院情報学研究科知能情報学専攻
(主査)教授奥乃博, 教授河原達也, 准教授 CUTURI CAMETO Marco, 講師吉井和佳
学位規則第4条第1項該当

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36

Gordon, Neil. "Bayesian methods for tracking". Thesis, Imperial College London, 1993. http://hdl.handle.net/10044/1/7783.

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37

Yuan, Lin. "Bayesian nonparametric survival analysis". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq22253.pdf.

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Chambers, Brian D. "Adaptive Bayesian information filtering". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0007/MQ45945.pdf.

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Kim, Yong Ku. "Bayesian multiresolution dynamic models". Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1180465799.

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40

Thouin, Frédéric. "Bayesian inference in networks". Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=104476.

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Bayesian inference is a method that can be used to estimate an unknown and/or unobservable parameter based on evidence that is accumulated over time.In this thesis, we apply Bayesian inference techniques in the context of two network-based problems.First, we consider multi-target tracking in networks with superpositional sensors, i.e., sensors that generate measurements equal to the sum of individual contributions of each target.We derive a tractable form for a novel moment-based multi-target filter called the Additive Likelihood Moment (ALM) filter. We show, through simulations, that our particle approximation of the ALM filter is more accurate and computationally efficient than Markov chain Monte Carlo-based particle methods to perform radio-frequency (RF) tomographic tracking of multiple targets.The second problem we study is multi-path available bandwidth estimation in computer networks.We propose a probabilistic-rate-based definition for the available bandwidth, probabilistic available bandwidth (PAB), that addresses flaws of the classical utilization-based definition and existing estimation tools. We design a network-wide estimation tool that uses factor graphs, belief propagation and adaptive sampling to minimize the overhead. We deploy our tool on the Planet Lab network and show that it can produce accurate estimates of the PAB and achieve significant gains (over 70%) in terms of measurement overhead and latency over a popular estimation tool (Pathload). We extend our tool to i) track PAB in time and ii) use chirps to further reduce the number of required measurements by over 80%. Our simulations and online experiments demonstrate that our tracking algorithm is more accurate than block-based approaches without any significant additional complexity.
L'inférence bayésienne est une méthode qui peut être utilisée pour estimer des paramètres inconnus et/ou inobservables à partir de preuves accumulées au fil du temps. Dans cette thèse, nous appliquons les techniques d'inférence bayésienne à deux problèmes de réseautique.Premièrement, nous considérons la poursuite de plusieurs cibles dans des réseaux de capteurs où les mesures générées sont égales à la somme des contributions individuelles de chaque cible. Nous obtenons une forme traitable pour un filtre multi-cibles appelé filtre Additive Likelihood Moment (ALM). Nous montrons, au moyen de simulations, que notre approximation particulaire du filtre ALM est plus précise et efficace que les méthodes particulaires de Monte-Carlo par chaînes de Markov pour effectuer une poursuite tomographique de plusieurs cibles à l'aide de radiofréquences.Le deuxième problème que nous étudions est l'estimation simultanée pour plusieurs chemins de bande passante disponible dans les réseaux informatiques. Nous proposons une définition probabiliste de la bande passante disponible, probabilistic available bandwidth (PAB), qui vise a corriger les failles de i) la définition classique fondée sur l'utilisation et ii) des outils d'estimation existants. Nous concevons un outil d'estimation pour l'ensemble du réseau qui utilise les réseaux bayésiens, la propagation de croyance et d'échantillonnage adapté pour minimiser le surdébit. Nous validons notre outil sur le réseau Planet Lab et montrons qu'il peut produire des estimations précises de la PAB et procure des gains significatifs (plus de 70%) en termes de surdébit et de latence en comparaison avec un outil d'estimation populaire (Pathload). Nous proposons ensuite une extension à notre outil pour i) suivre la PAB dans le temps et ii) utiliser les ``chirps'' pour réduire davantage le nombre de mesures requises par plus de 80%. Nos simulations et expériences en ligne montrent que notre algorithme de suivi est plus précis, sans complexité supplémentaire notable, que les approches qui traitent l'information en bloc sans modèle dynamique.

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41

Vines, Susan Karen. "Bayesian computation in epidemiology". Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.285259.

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42

Adami, K. Z. "Bayesian inference and deconvolution". Thesis, University of Cambridge, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.595341.

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This thesis is concerned with the development of Bayesian methods for inference and deconvolution. We compare and contrast different Bayesian methods for model selection, specifically Markov Chain Monte Carlo methods (MCMC) and Variational methods and their application to medical and industrial problems. In chapter 1, the Bayesian framework is outlined. In chapter 2 the different methods for Bayesian model selection are introduced and we assess each method in turn. Problems with MCMC methods and Variational methods are highlighted, before a new method which combines the strengths of both the MCMC methods and the Variational methods is developed. Chapter 3 applies the inferential methods described in chapter 2 to the problem of interpolation, before a regression neural network is implemented and tested on a set of data from the microelectronics industry. Chapter 4 applies the interpolation methods developed in chapter three to characterise the electrical nature of the testing site in the integrated circuit (IC) manufacturing process. Chapter 5 describes Independent Component Analysis (ICA) as a solution to the bilinear decomposition problem and its application to Magnetic Resonance Imaging. This chapter also compares and contrasts various Bayesian algorithms for the bilinear problem with a non-Bayesian MUSIC algorithm. Chapter 6 describes various models for the deconvolution of images including a regression network. The ICA model of chapter 5 is then extended to the deconvolution and blind deconvolution problems with the addition of intrinsic correlation functions.

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43

Bridle, S. L. "Bayesian methods in cosmology". Thesis, University of Cambridge, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.596905.

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This thesis is concerned with the amount and distribution of dark matter in the universe and makes use of Bayesian techniques to extract maximal information. I have been using two independent approaches. I have (i) compared and combined cosmological parameter estimates from various cosmological probes and (ii) I have developed a method for estimating the mass distribution in clusters of galaxies using gravitational lensing. The first approach tests cosmological theories and estimates the cosmological parameters, including the total amount of matter. The second produces maps of the dark matter in the largest gravitationally bound structures in the universe. Chapter 3 is also published as 'Cosmological Parameters from Cluster Abundances, CMB and IRAS' by Bridle et al. 1999 (MNRAS, 310, 565). Chapter 4 is also available as 'Cosmological Parameters from Velocities, CMB and Supernovae' by Bridle et al. 2000 (astro-ph/0006170, MNRAS accepted). Chapter 5 contains the work presented in Lahav, Bridle, Hobson, Lasenby and Sodré Jr. 2000 (MNRAS, 315, L45) entitled 'Bayesian 'Hyper-Parameters' Approach to Joint Estimation: the Hubble Constant from CMB Measurements' and further demonstrates the properties of this approach by applying it to toy models. Part II concerns maximum-entropy reconstruction of mass distributions from weak gravitational lensing data and consists of two chapters. Chapter 6 sets out the basic method, also published in 'A maximum-entropy method for reconstructing the projected mass distribution of gravitational lenses' by Bridle et al. 1998 (MNRAS, 299, 895). Chapter 7 details extensions to this work, also shortly to become available in Bridle et al. 2000, entitled 'Maximum-Entropy Reconstruction of Gravitational Lenses using Shear and/or Magnification Data'. Future directions are suggested in the conclusions to each part and in the Concluding Remarks.

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44

Isheden, Gabriel. "Bayesian Hierarchic Sample Clustering". Thesis, KTH, Matematik (Inst.), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-168316.

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This report presents a novel algorithm for hierarchical clustering called Bayesian Sample Clustering (BSC). BSC is a single linkage algorithm that uses data samples to produce a predictive distribution for each sample. The predictive distributions are compared using the Chan-Darwiche distance, a metric for finite probability distributions, to produce a hierarchy of samples. The implemented version of BSC is found at https://github.com/Skjulet/Bayesian Sample Clustering.
Denna rapport presenterar en ny algoritm för hierarkisk klustring, Bayesian Sample Clustering (BSC). BSC är en single-linkage algoritm som använder stickprov av data för att skapa en prediktiv fördelning för varje stickprov. De prediktiva fördelningarna jämförs med Chan-Darwiche avståndet, en metrik över ändliga sannolikhetsfördelningar, vilket möjliggör skapandet av en hierarki av kluster. BSC finns i implementerad version på https://github.com/Skjulet/Bayesian Sample Clustering.

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45

Frühwirth-Schnatter, Sylvia. "On Fuzzy Bayesian Inference". Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1990. http://epub.wu.ac.at/384/1/document.pdf.

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In the paper at hand we apply it to Bayesian statistics to obtain "Fuzzy Bayesian Inference". In the subsequent sections we will discuss a fuzzy valued likelihood function, Bayes' theorem for both fuzzy data and fuzzy priors, a fuzzy Bayes' estimator, fuzzy predictive densities and distributions, and fuzzy H.P.D .-Regions. (author's abstract)
Series: Forschungsberichte / Institut für Statistik

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46

Zhang, Yifan. "Bayesian Adaptive Clinical Trials". Thesis, Harvard University, 2014. http://nrs.harvard.edu/urn-3:HUL.InstRepos:13070079.

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Bayesian adaptive designs are emerging as popular approach to develop adaptive clinical trials. In this dissertation, I describe the mathematical steps for computing the theoretical optimal adaptive designs in biomarker-integrated trials and in trials with survival outcomes. Section 1 discusses the optimal design in personalized medicine. The optimal design maximizes the expected trial utility given any pre-specified utility function, though the discussion here focuses on maximizing responses within a given patient horizon. This work provides absolute benchmark for the evaluation of trial designs in targeted therapy with binary treatment outcomes. While treatment efficacy can be measured by a short-term binary outcome in many phase II and phase III trials, patients' progression-free survival time is with significant importance in cancer clinical trials. However, it is often difficult to make a design adaptive to survival outcomes because of the long observation time. In Section 2, an optimal adaptive design is developed so that treatment assignment decision for later patients can be made with complete or partial survival outcomes of early patients. The design also maximizes the expected trial utility given any pre-specified utility function that is of clinical importance. In this section, the focus is on maximizing the expected progression-free survival time. Both Sections1 and 2 include examples of comparing adaptive designs, such as the bayesian adaptive randomization and the play-the-winner rule, in terms of the expected trial utility with respect to the best achievable result. In Section 3, a simulation-based p-value is proposed and can be used to conduct frequentist analysis of Bayesian adaptive clinical trials. The optimal Bayesian design is compared to the equal randomization design in terms of the Type I error and the statistical power. With a fixed trial size and Type I error, the power of the equal randomization design depends on the difference in treatment efficacy, meanwhile the power of the optimal Bayesian design also depends on the size of the patient horizon.

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47

Quintana, José Mario. "Multivariate Bayesian forecasting models". Thesis, University of Warwick, 1987. http://wrap.warwick.ac.uk/34805/.

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This thesis concerns theoretical and practical Bayesian modelling of multivariate time series. Our main goal is to intruduce useful, flexible and tractable multivariate forecasting models and provide the necessary theory for their practical implementation. After a brief review of the dynamic linear model we formulate a new matrix-v-ariate generalization in which a significant part of the variance-covariance structure is unknown. And a new general algorithm, based on the sweep operator is provided for its recursive implementation. This enables important advances to be made in long-standing problems related with the specification of the variances. We address the problem of plug-in estimation and apply our results in the context of dynamic linear models. We extend our matrix-variate model by considering the unknown part of the variance-covariance structure to be dynamic. Furthermore, we formulate the dynamic recursive model which is a general counterpart of fully recursive econometric models. The latter part of the dissertation is devoted to modelling aspects. The usefulness of the methods proposed is illustrated with several examples involving real and simulated data.

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Upsdell, M. P. "Bayesian inference for functions". Thesis, University of Nottingham, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.356022.

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Khantadze, Davit. "Essays on Bayesian persuasion". Thesis, University of Warwick, 2017. http://wrap.warwick.ac.uk/104204/.

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Chapter 1 reviews the literature about the bayesian persuasion. It first describes two main approaches to bayesian persuasion: concavification approach and information design approach. Next I consider some extensions to the basic model of bayesian persuasion, like competition between different senders, privately informed receiver and dynamic bayesian persuasion. Some other contributions reviewed include costly bayesian persuasion and bayesian persuasion when receiver’s optimal action is only a function of an expected state. Chapter 2 deals with two-dimensional bayesian persuasion. In this chapter I investigate a model when the receiver has to make two decisions. I am interested in optimal signal structures for the sender. I describe the upper bound of sender’s payoff in terms of his payoff when only marginal distributions of two dimensions are known. Completely characterise optimal simultaneous and sequential signal structures, when each dimension has binary states. This approach extends concavification approach to bigger state space, than explored in previous contributions to bayesian persuasion. Finally I characterise optimal sequential signal structure when there are three states for each dimension. In chapter 3 I investigate together with my co-author the effect of absence of common knowledge on the outcomes of coordination games in a laboratory experiment. In our experiment, around 76% of the subjects have chosen the payoff-dominant equilibrium strategy despite the absence of common knowledge. However, 9% of the players had first-order beliefs that lead to coordination failure and another 9% exhibited coordination failure due to higher-order beliefs.

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50

Joseph, Joshua Mason. "Nonparametric Bayesian behavior modeling". Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45263.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.
Includes bibliographical references (p. 91-94).
As autonomous robots are increasingly used in complex, dynamic environments, it is crucial that the dynamic elements are modeled accurately. However, it is often difficult to generate good models due to either a lack of domain understanding or the domain being intractably large. In many domains, even defining the size of the model can be a challenge. While methods exist to cluster data of dynamic agents into common motion patterns, or "behaviors," assumptions of the number of expected behaviors must be made. This assumption can cause clustering processes to under-fit or over-fit the training data. In a poorly understood domain, knowing the number of expected behaviors a priori is unrealistic and in an extremely large domain, correctly fitting the training data is difficult. To overcome these obstacles, this thesis takes a Bayesian approach and applies a Dirichlet process (DP) prior over behaviors, which uses experience to reduce the likelihood of over-fitting or under-fitting the model complexity. Additionally, the DP maintains a probability mass associated with a novel behavior and can address countably infinite behaviors. This learning technique is applied to modeling agents driving in an urban setting. The learned DP-based driver behavior model is first demonstrated on a simulated city. Building on successful simulation results, the methodology is applied to GPS data of taxis driving around Boston. Accurate prediction of future vehicle behavior from the model is shown in both domains.
by Joshua Mason Joseph.
S.M.

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