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Dissertations / Theses on the topic 'Bayesian'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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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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2

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.<br>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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3

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

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4

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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5

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 po

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6

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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7

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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8

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.<br>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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<p>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 categori

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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

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12

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

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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

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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

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15

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 p

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16

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

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<p> 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 filter

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17

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 pri

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18

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.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 167-175).<br>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 allow

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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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20

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 term

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21

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

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22

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 probabilist

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23

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

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24

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 equation

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25

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

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No abstract available<br>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 G

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26

Dau, Hai Dang. "Sequential Bayesian Computation." Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAG006.

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Cette thèse est composée de deux parties. La première concerne les échantillonneurs dits de Monte-Carlo séquentiel (les échantillonneurs SMC). Il s'agit d'une famille d'algorithmes pour produire des échantillons venant d'une suite de distributions, grâce à une combinaison de l'échantillonnage pondéré et la méthode de Monte-Carlo par chaîne de Markov (MCMC). Nous proposons une version améliorée qui exploite les particules intermédiaires engendrées par l'application de plusieurs pas de MCMC. Elle a une meilleure performance, est plus robuste et permet la construction d'estimateurs de la variance

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27

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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28

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

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29

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

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30

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<br>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 pred

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31

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 (

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32

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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33

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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<p>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. </p><p>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 p

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34

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-nor

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35

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-spl

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36

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)<br>0048<br>新制・課程博士<br>博士(情報学)<br>甲第18412号<br>情博第527号<br>新制||情||93(附属図書館)<br>31270<br>京都大学大学院情報学研究科知能情報学専攻<br>(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<br>学位規則第4条第1項該当

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37

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

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38

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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39

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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40

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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41

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 par

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42

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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43

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 M

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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

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45

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.<br>Denna rapport presenterar en ny algoritm för hierarkisk klustring, Bayesian Sample Clustering (BSC). BSC är en single-link

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46

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)<br>Series: Forschungsberichte / Institut für Statistik

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47

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 des

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48

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 prob

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49

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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50

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 chap

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