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Dissertations / Theses on the topic 'Phylogeny Bayesian statistical decision theory'

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Published: 4 June 2021

Last updated: 11 February 2022

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1

Liu, Liang. "Reconstructing posterior distributions of a species phylogeny using estimated gene tree distributions." Columbus, Ohio : Ohio State University, 2006. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1155754980.

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2

Lepage, Thomas. "The impact of variable evolutionary rates on phylogenetic inference : a Bayesian approach." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=103264.

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In this dissertation, we explore the effect of variable evolutionary rates on phylogenetic inference. In the first half of the thesis are introduced the biological fundamentals and the statistical framework that will be used throughout the thesis. The basic concepts in phylogenetics and an overview of Bayesian inference are presented in Chapter 1. In Chapter 2, we survey the models that are already used for rate variation. We argue that the CIR process---a diffusion process widely used in finance---is the best suited for applications in phylogenetics, for both mathematical and computational reasons. Chapter 3 shows how evolutionary rate models are incorporated to DNA substitution models. We derive the general formulae for transition probabilities of substitutions when the rate is a continuous-time Markov chain, a diffusion process or a jump process (a diffusion process with discrete jumps).
The second half of the thesis is dedicated to applications of variable evolutionary rate models in two different contexts. In Chapter 4, we use the CIR process to model heterotachy, an evolutionary hypothesis according to which positions of an alignment may evolve at rates that vary with time differently from site to site. A comparison the CIR process with the covarion---a widely-used heterotachous model---on two different data sets allows us to conclude that the CIR provides a significantly better fit. Our approach, based on a Bayesian mixture model, enables us to determine the level of heterotachy at each site. Finally, the impact of variable evolutionary rates on divergence time estimation is explored in Chapter 5.
Several models, including the CIR process are compared on three data sets. We find that autocorrelated models (including the CIR) provide the best fits.

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3

Carvalho, Ricardo Durães de 1985. "Investigação e caracterização filogenética de Coronavírus na biota de aves silvestres e sinantrópicas provenientes das regiões Sul e Sudeste do Brasil." [s.n.], 2015. http://repositorio.unicamp.br/jspui/handle/REPOSIP/316639.

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Orientadores: Clarice Weis Arns, Márcia Bianchi dos Santos
Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Biologia
Made available in DSpace on 2018-08-27T11:13:27Z (GMT). No. of bitstreams: 1 Carvalho_RicardoDuraesde_D.pdf: 3518273 bytes, checksum: 7b6f8b159eb057429823e23f6852c29b (MD5) Previous issue date: 2015
Resumo: A evolução e a dinâmica populacional dos Coronavírus (CoVs) ainda permanecem pouco exploradas. No presente estudo, análises filogenéticas e de filogeografia foram conduzidas para investigar a dinâmica evolutiva dos CoVs detectados em aves silvestres e sinantrópicas. Um total de 500 amostras, que inclui os suabes traqueais e cloacais coletados de 312 aves silvestres pertencentes a 42 espécies, foram analisadas através da RT-qPCR. Sessenta e cinco amostras (13%) provenientes de 23 espécies foram positivas para o Coronavírus aviário (AvCoV). Trezentos e duas amostras foram investigadas para a pesquisa do Pan-Coronavírus (Pan-CoV) através do nPCR, destas, 17 (5,6%) foram positivas, sendo que 11 foram detectadas em espécies diferentes. Análises filogenéticas dos AvCoVs revelaram que as sequências de DNA das amostras coletadas no Brasil não agruparam com nenhuma das sequências do gene Spike (S1) dos AvCoVs depositados no banco de dados GenBank. Análise Bayesiana estimou uma variante do AvCoV proveniente da Suécia (1999) como o ancestral comum mais recente dos AvCoVs detectados neste estudo. Além disso, as análises realizadas através do "Bayesian Skyline Plot" (BSP) inferiram um aumento na dinâmica da população demográfica do AvCoV em diferentes espécies de aves silvestres e sinantrópicas. As análises filogenéticas do Pan-CoV mostrou que a maioria das amostras se agruparam com o Vírus da Hepatite Murina A59 (MHV A59), CoV pertencente ao grupo dos Beta-CoVs. Uma amostra [CoV detectado em Amazona vinacea(Papagaio-de-peito-roxo)] se agrupou com um CoV de Suínos, o PCoV HKU15, que pertence ao gênero Delta-CoV, ainda não relatado na América do Sul. Nossos achados sugerem que as aves podem ser novos potenciais hospedeiros responsáveis pela propagação e disseminação de diferentes CoVs para diferentes espécies de animais
Abstract: The evolution and population dynamics of Coronaviruses (CoVs) still remain underexplored. In the present study, phylogenetic and phylogeographic analyseswere conducted to investigate the evolutionary dynamics of CoV detected in wild and synanthropic birds. A total of 500 samples, including tracheal and cloacal swabs collected from 312 wild birds belonging to 42species, were analysed by RT-qPCR. A total of 65 samples from 23bird species were positive for Avian Coronaviruses (AvCoVs).Three hundred and two samples were screened for the Pan-Coronavirus (Pan-CoV) through the nPCR, 17 (5.6%) were positive, being that 11 were detected in different species. AvCoVs phylogenetic analyses revealed that the DNA sequences from samples collected in Brazil did not cluster with any of the AvCoV S1 gene sequences deposited in the GenBank database. Bayesian framework analysis estimated an AvCoV strain from Sweden (1999) as the most recent common ancestor of the AvCoVs detected in this study. Furthermore, Bayesian Skyline Plot (BSP) analysis inferred an increase in the AvCoV dynamic demographic population in different wild and synanthropic bird species. Phylogenetic analysis of the Pan-CoV showed that most of the samples clustered with the Murine Hepatitis Virus A59 strain (MHV A59) belong to the BetaCoV group. Besides, one of our samples [CoV detected in Amazona vinacea (parrot-breasted-purple)] clustered with a CoV isolated from pigs, PCoV HKU15, belonging to the DeltaCoV genus, still not reported in South America. Our findings suggest that birds may be potential new hosts responsible for spreading of different CoVs for different species of animals
Doutorado
Microbiologia
Doutor em Genetica e Biologia Molecular

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4

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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5

Luo, Wuben. "A comparative assessment of Dempster-Shafer and Bayesian belief in civil engineering applications." Thesis, University of British Columbia, 1988. http://hdl.handle.net/2429/28500.

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The Bayesian theory has long been the predominate method in dealing with uncertainties in civil engineering practice including water resources engineering. However, it imposes unnecessary restrictive requirements on inferential problems. Concerns thus arise about the effectiveness of using Bayesian theory in dealing with more general inferential problems. The recently developed Dempster-Shafer theory appears to be able to surmount the limitations of Bayesian theory. The new theory was originally proposed as a pure mathematical theory. A reasonable amount of work has been done in trying to adopt this new theory in practice, most of this work being related to inexact inference in expert systems and all of the work still remaining in the fundamental stage. The purpose of this research is first to compare the two theories and second to try to apply Dempster-Shafer theory in solving real problems in water resources engineering. In comparing Bayesian and Dempster-Shafer theory, the equivalent situation between these two theories under a special situation is discussed first. The divergence of results from Dempster-Shafer and Bayesian approaches under more general situations where Bayesian theory is unsatisfactory is then examined. Following this, the conceptual difference between the two theories is argued. Also discussed in the first part of this research is the issue of dealing with evidence including classifying sources of evidence and expressing them through belief functions. In attempting to adopt Dempster-Shafer theory in engineering practice, the Dempster-Shafer decision theory, i.e. the application of Dempster-Shafer theory within the framework of conventional decision theory, is introduced. The application of this new decision theory is demonstrated through a water resources engineering design example.
Applied Science, Faculty of
Civil Engineering, Department of
Graduate

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6

Yeo, Yeongseo. "Bayesian scientific methodology : a naturalistic approach /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3074459.

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7

Pei, Xin, and 裴欣. "Bayesian approach to road safety analyses." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B46591989.

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8

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

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9

Feng, Chunyao Seaman John Weldon. "Bayesian evaluation of surrogate endpoints." Waco, Tex. : Baylor University, 2006. http://hdl.handle.net/2104/4187.

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10

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

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11

Thaithara, Balan Sreekumar. "Bayesian methods for astrophysical data analysis." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.607847.

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12

Fenwick, Elisabeth. "An iterative framework for health technology assessment employing Bayesian statistical decision theory." Thesis, University of York, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.423768.

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13

Sheng, Ru. "A Bayesian analysis of hypothesis testing problems with skewed alternatives." [Milwaukee, Wis.] : e-Publications@Marquette, 2009. http://epublications.marquette.edu/dissertations_mu/23.

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14

Shon, Aaron P. "Bayesian cognitive models for imitation /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/7013.

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15

Ramachandran, Sowmya. "Theory refinement of Bayesian networks with hidden variables /." Digital version accessible at:, 1998. http://wwwlib.umi.com/cr/utexas/main.

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16

Cannon, Stephen J. "Analysis of the relationship between partially dynamic Bayesian network architecture and inference algorithm effectiveness." Fairfax, VA : George Mason University, 2007. http://hdl.handle.net/1920/3181.

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Thesis (M.S.)--George Mason University, 2007.
Vita: p. 192. Thesis director: Kathryn Blackmond Laskey. Submitted in partial fulfillment of the requirements for the degree of Master of Science in Systems Engineering. Title from PDF t.p. (viewed Aug. 13, 2008). Additional zip folders contain software, thesis defense powerpoint and analysis documents. Includes bibliographical references (p. 190-191). Also issued in print.

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17

Klemens, Ben Jackson Matthew O. "Information aggregation, with application to monotone ordering, advocacy, and conviviality /." [Pasadena, Calif. : California Institute of Technology], 2003. http://www.fluff.info/klemens/.

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18

Nono, Bertin. "Applications of Bayesian statistics a thesis presented to the faculty of the Graduate School, Tennessee Technological University /." Click to access online, 2009. http://proquest.umi.com/pqdweb?index=3&did=1769600741&SrchMode=1&sid=3&Fmt=6&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1250263533&clientId=28564.

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19

Srivastava, Santosh. "Bayesian minimum expected risk estimation of distributions for statistical learning /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/6765.

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20

Armstrong, Helen School of Mathematics UNSW. "Bayesian estimation of decomposable Gaussian graphical models." Awarded by:University of New South Wales. School of Mathematics, 2005. http://handle.unsw.edu.au/1959.4/24295.

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This thesis explains to statisticians what graphical models are and how to use them for statistical inference; in particular, how to use decomposable graphical models for efficient inference in covariance selection and multivariate regression problems. The first aim of the thesis is to show that decomposable graphical models are worth using within a Bayesian framework. The second aim is to make the techniques of graphical models fully accessible to statisticians. To achieve these aims the thesis makes a number of statistical contributions. First, it proposes a new prior for decomposable graphs and a simulation methodology for estimating this prior. Second, it proposes a number of Markov chain Monte Carlo sampling schemes based on graphical techniques. The thesis also presents some new graphical results, and some existing results are reproved to make them more readily understood. Appendix 8.1 contains all the programs written to carry out the inference discussed in the thesis, together with both a summary of the theory on which they are based and a line by line description of how each routine works.

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21

Koh, You Beng, and 辜有明. "Bayesian analysis in Markov regime-switching models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B48521644.

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van Norden and Schaller (1996) develop a standard regime-switching model to study stock market crashes. In their seminal paper, they use the maximum likelihood estimation to estimate the model parameters and show that a two-regime speculative bubble model has significant explanatory power for stock market returns in some observed periods. However, it is well known that the maximum likelihood estimation can lead to bias if the model contains multiple local maximum points or the estimation starts with poor initial values. Therefore, a better approach to estimate the parameters in the regime-switching models is to be found. One possible way is the Bayesian Gibbs-sampling approach, where its advantages are well discussed in Albert and Chib (1993). In this thesis, the Bayesian Gibbs-sampling estimation is examined by using two U.S. stock datasets: CRSP monthly value-weighted index from Jan 1926 to Dec 2010 and S&P 500 index from Jan 1871 to Dec 2010. It is found that the Gibbs-sampling estimation explains the U.S. data better than the maximum likelihood estimation. Moreover, the existing standard regime-switching speculative behaviour model is extended by considering the time-varying transition probabilities which are governed by the first-order Markov chain. It is shown that the time-varying first-order transition probabilities of Markov regime-switching speculative rational bubbles can lead stock market returns to have a second-order Markov regime. In addition, a Bayesian Gibbs-sampling algorithm is developed to estimate the parameters in the second-order two-state Markov regime-switching model.
published_or_final_version
Statistics and Actuarial Science
Doctoral
Doctor of Philosophy

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22

Tra, Yolande Vololonirina. "Bayesian analysis for avian nest survival models /." free to MU campus, to others for purchase, 2000. http://wwwlib.umi.com/cr/mo/fullcit?p9974691.

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23

Spann, Melissa Elizabeth Seaman John Weldon. "Bayesian adaptive designs for non-inferiority and dose selection trials." Waco, Tex. : Baylor University, 2006. http://hdl.handle.net/2104/4207.

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24

Scheffler, Carl. "Applied Bayesian inference : natural language modelling and visual feature tracking." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610748.

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25

Atherton, Juli. "Bayesian optimal design for changepoint problems." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=102954.

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We consider optimal design for changepoint problems with particular attention paid to situations where the only possible change is in the mean. Optimal design for changepoint problems has only been addressed in an unpublished doctoral thesis, and in only one journal article, which was in a frequentist setting. The simplest situation we consider is that of a stochastic process that may undergo a, change at an unknown instant in some interval. The experimenter can take n measurements and is faced with one or more of the following optimal design problems: Where should these n observations be taken in order to best test for a change somewhere in the interval? Where should the observations be taken in order to best test for a change in a specified subinterval? Assuming that a change will take place, where should the observations be taken so that that one may best estimate the before-change mean as well as the after-change mean? We take a Bayesian approach, with a risk based on squared error loss, as a design criterion function for estimation, and a risk based on generalized 0-1 loss, for testing. We also use the Spezzaferri design criterion function for model discrimination, as an alternative criterion function for testing. By insisting that all observations are at least a minimum distance apart in order to ensure rough independence, we find the optimal design for all three problems. We ascertain the optimal designs by writing the design criterion functions as functions of the design measure, rather than of the designs themselves. We then use the geometric form of the design measure space and the concavity of the criterion function to find the optimal design measure. There is a straightforward correspondence between the set of design measures and the set of designs. Our approach is similar in spirit, although rather different in detail, from that introduced by Kiefer. In addition, we consider design for estimation of the changepoint itself, and optimal designs for the multipath changepoint problem. We demonstrate why the former problem most likely has a prior-dependent solution while the latter problems, in their most general settings, are complicated by the lack of concavity of the design criterion function.
Nous considérons, dans cette dissertation, les plans d'expérience bayésiens optimauxpour les problèmes de point de rupture avec changement d'espérance. Un cas de pointde rupture avec changement d'espérance à une seule trajectoire se présente lorsqu'uneséquence de données est prélevée le long d'un axe temporelle (ou son équivalent) etque leur espérance change de valeur. Ce changement, s'il survient, se produit à unendroit sur l'axe inconnu de l'expérimentateur. Cet endroit est appelé "point derupture". Le fait que la position du point de rupture soit inconnue rend les tests etl'inférence difficiles dans les situations de point de rupture à une seule trajectoire.

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26

Sun, Wei. "Efficient inference for hybrid Bayesian networks." Fairfax, VA : George Mason University, 2007. http://hdl.handle.net/1920/2952.

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Thesis (Ph. D.)--George Mason University, 2007.
Title from PDF t.p. (viewed Jan. 22, 2008). Thesis director: KC Chang. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Technology. Vita: p. 117. Includes bibliographical references (p. 108-116). Also available in print.

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27

Yen, Peng-Fang. "Some problems in Bayesian group decisions." Virtual Press, 1992. http://liblink.bsu.edu/uhtbin/catkey/845934.

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One employs the mathematical analysis of decision making when the state of nature is uncertain but further information about it can be obtained by experimentation. Bayesian Decision Theory concerns practical problems of decision making under conditions of uncertainty and also requires the use of statistical and mathematical methods.In this thesis, some basic risk sharing and group decision concepts are provided. Risk is the expected value of the Loss Function of Bayesian Estimators. Group decisions consider situations in which the individuals need to agree both on utilities for consequences and on conditional probability assessments for different experimental outcomes.
Department of Mathematical Sciences

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28

Kim, Seong W. "Bayesian model selection using intrinsic priors for commonly used models in reliability and survival analysis /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9841159.

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29

So, Moon-tong. "Applications of Bayesian statistical model selection in social science research." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B39312951.

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30

Ignatieva, Ekaterina. "Adaptive Bayesian sampling with application to 'bubbles'." Connect to e-thesis, 2008. http://theses.gla.ac.uk/356/.

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Thesis (MSc(R)) - University of Glasgow, 2008.
MSc(R). thesis submitted to the Department of Mathematics, Faculty of Information and Mathematical Sciences, University of Glasgow, 2008. Includes bibliographical references.

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31

Zhu, Shaojuan. "Associative memory as a Bayesian building block /." Full text open access at:, 2008. http://content.ohsu.edu/u?/etd,655.

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32

Siu, Wai-shing. "On a subjective modelling of VaR fa Bayesian approach /." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22823785.

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33

Fung, Wing-kam Tony. "Analysis of outliers using graphical and quasi-Bayesian methods /." [Hong Kong] : University of Hong Kong, 1987. http://sunzi.lib.hku.hk/hkuto/record.jsp?B1236146X.

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34

Guidolin, Massimo. "Asset prices on Bayesian learning paths /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2000. http://wwwlib.umi.com/cr/ucsd/fullcit?p9975886.

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35

Chan, Chun-man, and 陳俊文. "On a topic of Bayesian analysis using scale mixtures distributions." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31223989.

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Chan, Chun-man. "On a topic of Bayesian analysis using scale mixtures distributions." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22665183.

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37

林達明 and Daming Lin. "Reliability growth models and reliability acceptance sampling plans from a Bayesian viewpoint." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1995. http://hub.hku.hk/bib/B3123429X.

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38

Ho, Man Wai. "Bayesian inference for models with monotone densities and hazard rates /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?ISMT%202002%20HO.

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Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2002.
Includes bibliographical references (leaves 110-114). Also available in electronic version. Access restricted to campus users.

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39

Van, Koten Chikako, and n/a. "Bayesian statistical models for predicting software effort using small datasets." University of Otago. Department of Information Science, 2007. http://adt.otago.ac.nz./public/adt-NZDU20071009.120134.

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The need of today�s society for new technology has resulted in the development of a growing number of software systems. Developing a software system is a complex endeavour that requires a large amount of time. This amount of time is referred to as software development effort. Software development effort is the sum of hours spent by all individuals involved. Therefore, it is not equal to the duration of the development. Accurate prediction of the effort at an early stage of development is an important factor in the successful completion of a software system, since it enables the developing organization to allocate and manage their resource effectively. However, for many software systems, accurately predicting the effort is a challenge. Hence, a model that assists in the prediction is of active interest to software practitioners and researchers alike. Software development effort varies depending on many variables that are specific to the system, its developmental environment and the organization in which it is being developed. An accurate model for predicting software development effort can often be built specifically for the target system and its developmental environment. A local dataset of similar systems to the target system, developed in a similar environment, is then used to calibrate the model. However, such a dataset often consists of fewer than 10 software systems, causing a serious problem in the prediction, since predictive accuracy of existing models deteriorates as the size of the dataset decreases. This research addressed this problem with a new approach using Bayesian statistics. This particular approach was chosen, since the predictive accuracy of a Bayesian statistical model is not so dependent on a large dataset as other models. As the size of the dataset decreases to fewer than 10 software systems, the accuracy deterioration of the model is expected to be less than that of existing models. The Bayesian statistical model can also provide additional information useful for predicting software development effort, because it is also capable of selecting important variables from multiple candidates. In addition, it is parametric and produces an uncertainty estimate. This research developed new Bayesian statistical models for predicting software development effort. Their predictive accuracy was then evaluated in four case studies using different datasets, and compared with other models applicable to the same small dataset. The results have confirmed that the best new models are not only accurate but also consistently more accurate than their regression counterpart, when calibrated with fewer than 10 systems. They can thus replace the regression model when using small datasets. Furthermore, one case study has shown that the best new models are more accurate than a simple model that predicts the effort by calculating the average value of the calibration data. Two case studies has also indicated that the best new models can be more accurate for some software systems than a case-based reasoning model. Since the case studies provided sufficient empirical evidence that the new models are generally more accurate than existing models compared, in the case of small datasets, this research has produced a methodology for predicting software development effort using the new models.

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40

Farrell, Patrick John. "Empirical Bayes estimation of small area proportions." Thesis, McGill University, 1991. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=70301.

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Due to the nature of survey design, the estimation of parameters associated with small areas is extremely problematic. In this study, techniques for the estimation of small area proportions are proposed and implemented. More specifically, empirical Bayes estimation methodologies, where random effects which reflect the complex structure of a multi-stage sample design are incorporated into logistic regression models, are derived and studied.
The proposed techniques are applied to data from the 1950 United States Census to predict local labor force participation rates of females. Results are compared with those obtained using unbiased and synthetic estimation approaches.
Using the proposed methodologies, a sensitivity analysis concerning the prior distribution assumption, conducted with a view toward outlier detection, is performed. The use of bootstrap techniques to correct measures of uncertainty is also studied.

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41

Metcalfe, Leanne N. "Bayesian methods in determining health burdens." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/31809.

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Thesis (Ph.D)--Biomedical Engineering, Georgia Institute of Technology, 2009.
Committee Chair: Vidakovic, Brani; Committee Member: Griffin, Paul; Committee Member: Kemp, Charlie; Committee Member: Sprigle, Stephen; Committee Member: Villivalam, Arun. Part of the SMARTech Electronic Thesis and Dissertation Collection.

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42

Ounpraseuth, Songthip T. Young Dean M. "Selected topics in statistical discriminant analysis." Waco, Tex. : Baylor University, 2006. http://hdl.handle.net/2104/4883.

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43

Cho, Hyun Cheol. "Dynamic Bayesian networks for online stochastic modeling." abstract and full text PDF (free order & download UNR users only), 2006. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3221394.

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44

Chiu, Jing-Er. "Applications of bayesian methods to arthritis research /." free to MU campus, to others for purchase, 2001. http://wwwlib.umi.com/cr/mo/fullcit?p3036813.

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45

Vlasakakis, Georgios. "Application of Bayesian statistics to physiological modelling." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610198.

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46

Endres, Dominik M. "Bayesian and information-theoretic tools for neuroscience." Thesis, St Andrews, 2006. http://hdl.handle.net/10023/162.

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47

So, Moon-tong, and 蘇滿堂. "Applications of Bayesian statistical model selection in social scienceresearch." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39312951.

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Avcioglu-Ayturk, Mubeccel Didem. "A simulation of Industry and occupation codes in 1970 and 1980 U.S Census." Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-060105-161730/.

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Huszár, Ferenc. "Scoring rules, divergences and information in Bayesian machine learning." Thesis, University of Cambridge, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648333.

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Lee, Suhwon. "Nonparametric bayesian density estimation with intrinsic autoregressive priors /." free to MU campus, to others for purchase, 2003. http://wwwlib.umi.com/cr/mo/fullcit?p3115565.

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