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Dissertations / Theses on the topic 'Bayesian statistical decision theory – Applications'

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Published: 10 December 2022
Last updated: 29 January 2023

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1

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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2

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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3

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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4

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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5

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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6

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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7

Lu, Jun. "Bayesian hierarchical models and applications in psychology research /." free to MU campus, to others for purchase, 2004. http://wwwlib.umi.com/cr/mo/fullcit?p3144437.

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8

Dümbgen, Moritz. "Extremal martingales with applications and a Bayesian approach to model selection." Thesis, University of Cambridge, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.708881.

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9

Higdon, David. "Spatial applications of Markov chain Monte Carlo for Bayesian inference /." Thesis, Connect to this title online; UW restricted, 1994. http://hdl.handle.net/1773/8942.

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10

Zhang, Yanwei. "A hierarchical Bayesian approach to model spatially correlated binary data with applications to dental research." Diss., Connect to online resource - MSU authorized users, 2008.

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11

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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12

Grimes, David B. "Learning by imitation and exploration : Bayesian models and applications in humanoid robotics /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/6879.

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13

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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14

McBride, John Jacob Bratcher Thomas L. "Conjugate hierarchical models for spatial data an application on an optimal selection procedure /." Waco, Tex. : Baylor University, 2006. http://hdl.handle.net/2104/3955.

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15

Southey, Richard. "Bayesian hierarchical modelling with application in spatial epidemiology." Thesis, Rhodes University, 2018. http://hdl.handle.net/10962/59489.

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Disease mapping and spatial statistics have become an important part of modern day statistics and have increased in popularity as the methods and techniques have evolved. The application of disease mapping is not only confined to the analysis of diseases as other applications of disease mapping can be found in Econometric and financial disciplines. This thesis will consider two data sets. These are the Georgia oral cancer 2004 data set and the South African acute pericarditis 2014 data set. The Georgia data set will be used to assess the hyperprior sensitivity of the precision for the uncorrelated heterogeneity and correlated heterogeneity components in a convolution model. The correlated heterogeneity will be modelled by a conditional autoregressive prior distribution and the uncorrelated heterogeneity will be modelled with a zero mean Gaussian prior distribution. The sensitivity analysis will be performed using three models with conjugate, Jeffreys' and a fixed parameter prior for the hyperprior distribution of the precision for the uncorrelated heterogeneity component. A simulation study will be done to compare four prior distributions which will be the conjugate, Jeffreys', probability matching and divergence priors. The three models will be fitted in WinBUGS® using a Bayesian approach. The results of the three models will be in the form of disease maps, figures and tables. The results show that the hyperprior of the precision for the uncorrelated heterogeneity and correlated heterogeneity components are sensitive to changes and will result in different results depending on the specification of the hyperprior distribution of the precision for the two components in the model. The South African data set will be used to examine whether there is a difference between the proper conditional autoregressive prior and intrinsic conditional autoregressive prior for the correlated heterogeneity component in a convolution model. Two models will be fitted in WinBUGS® for this comparison. Both the hyperpriors of the precision for the uncorrelated heterogeneity and correlated heterogeneity components will be modelled using a Jeffreys' prior distribution. The results show that there is no significant difference between the results of the model with a proper conditional autoregressive prior and intrinsic conditional autoregressive prior for the South African data, although there are a few disadvantages of using a proper conditional autoregressive prior for the correlated heterogeneity which will be stated in the conclusion.
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16

Ridgeway, Gregory Kirk. "Generalization of boosting algorithms and applications of Bayesian inference for massive datasets /." Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/8986.

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17

Rodríguez, Iván. "Bayesian analysis for Cox's proportional hazard model with error effect and applications to accelerated life testing data." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2007. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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18

Poole, David. "Bayesian inference for noninvertible deterministic simulation models, with application to bowhead whale assessment /." Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/8981.

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19

Chen, Hongshu. "Sampling-based Bayesian latent variable regression methods with applications in process engineering." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1189650596.

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20

Carlin, Patricia M. Seaman John Weldon. "Bayesian inference for correlated binary data with an application to diabetes complication progression." Waco, Tex. : Baylor University, 2006. http://hdl.handle.net/2104/4829.

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21

Ardia, David. "Financial risk management with Bayesian estimation of GARCH models theory and applications." Berlin Heidelberg Springer, 2008. http://d-nb.info/987538780/04.

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22

Sun, Xiaoqian. "Bayesian spatial data analysis with application to the Missouri Ozark forest ecosystem project." Diss., Columbia, Mo. : University of Missouri-Columbia, 2006. http://hdl.handle.net/10355/4477.

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Thesis (Ph.D.)--University of Missouri-Columbia, 2006.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (May 1, 2007) Vita. Includes bibliographical references.
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23

Huang, Zhen. "Application of Bayesian approach on ground motion attenuation relationship for Wenchuan Earthquake." Thesis, University of Macau, 2017. http://umaclib3.umac.mo/record=b3691515.

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24

Li, Yuan. "The new development of econometrics and its applications in financial markets." Diss., Online access via UMI:, 2009.

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25

Liu, Jie. "Novel Bayesian Methods for Disease Mapping: An Application to Chronic Obstructive Pulmonary Disease." Link to electronic thesis, 2002. http://www.wpi.edu/Pubs/ETD/Available/etd-0501102-110350.

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Thesis (M.S.)--Worcester Polytechnic Institute.
Keywords: latent class model; Poisson regression model; Metropolis-Hastings sampler; order restriction; disease mapping. Includes bibliographical references.
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26

Erhardt, Erik Barry. "Bayesian Simultaneous Intervals for Small Areas: An Application to Mapping Mortality Rates in U.S. Health Service Areas." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0105104-195633/.

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Thesis (M.S.) -- Worcester Polytechnic Institute.
Keywords: Poisson-Gamma Regression; MCMC; Bayesian; Small Area Estimation; Simultaneous Inference; Statistics Includes bibliographical references (p. 61-67).
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27

Jeon, Seonghye. "Bayesian data mining techniques in public health and biomedical applications." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43712.

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The emerging research issues in evidence-based healthcare decision-making and explosion of comparative effectiveness research (CER) are evident proof of the effort to thoroughly incorporate the rich data currently available within the system. The flexibility of Bayesian data mining techniques lends its strength to handle the challenging issues in the biomedical and health care domains. My research focuses primarily on Bayesian data mining techniques for non-traditional data in this domain, which includes, 1. Missing data: Matched-pair studies with fixed marginal totals with application to meta-analysis of dental sealants effectiveness. 2. Data with unusual distribution: Modeling spatial repeated measures with excess zeros and no covariates to estimate U.S. county level natural fluoride concentration. 3. Highly irregular data: Assess overall image regularity in complex wavelet domain to classify mammography image. The goal of my research is to strengthen the link from data to decisions. By using Bayesian data mining techniques including signal and image processing (wavelet analysis), hierarchical Bayesian modeling, clinical trials meta-analyses and spatial statistics, this thesis resolves challenging issues of how to incorporate data to improve the systems of health care and bio fields and ultimately benefit public health.
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28

Myers, James William. "Stochastic algorithms for learning with incomplete data an application to Bayesian networks /." Full text available online (restricted access), 1999. http://images.lib.monash.edu.au/ts/theses/Myers.pdf.

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29

Tang, Adelina Lai Toh. "Application of the tree augmented naive Bayes network to classification and forecasting /." [St. Lucia, Qld.], 2004. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe.pdf.

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30

Hoyt, Pamela J. "Discretization and learning of Bayesian Networks using stochastic search, with application to Base Realignment and Closure (BRAC)." Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3141.

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Thesis (Ph.D.)--George Mason University, 2008.
Vita: p. 183. Thesis director: Kathryn B. Laskey. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Technology. Title from PDF t.p. (viewed July 7, 2008). Includes bibliographical references (p. 168-182). Also issued in print.
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31

Zhang, Jing. "Bayesian spatial analysis with application to the Missouri Ozark Forest ecosystem project." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/6062.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2008.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 3, 2009) Vita. Includes bibliographical references.
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32

Han, Gang. "Modeling the output from computer experiments having quantitative and qualitative input variables and its applications." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1228326460.

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33

Oteniya, Lloyd. "Bayesian belief networks for dementia diagnosis and other applications : a comparison of hand-crafting and construction using a novel data driven technique." Thesis, University of Stirling, 2008. http://hdl.handle.net/1893/497.

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The Bayesian network (BN) formalism is a powerful representation for encoding domains characterised by uncertainty. However, before it can be used it must first be constructed, which is a major challenge for any real-life problem. There are two broad approaches, namely the hand-crafted approach, which relies on a human expert, and the data-driven approach, which relies on data. The former approach is useful, however issues such as human bias can introduce errors into the model. We have conducted a literature review of the expert-driven approach, and we have cherry-picked a number of common methods, and engineered a framework to assist non-BN experts with expert-driven construction of BNs. The latter construction approach uses algorithms to construct the model from a data set. However, construction from data is provably NP-hard. To solve this problem, approximate, heuristic algorithms have been proposed; in particular, algorithms that assume an order between the nodes, therefore reducing the search space. However, traditionally, this approach relies on an expert providing the order among the variables --- an expert may not always be available, or may be unable to provide the order. Nevertheless, if a good order is available, these order-based algorithms have demonstrated good performance. More recent approaches attempt to ''learn'' a good order then use the order-based algorithm to discover the structure. To eliminate the need for order information during construction, we propose a search in the entire space of Bayesian network structures --- we present a novel approach for carrying out this task, and we demonstrate its performance against existing algorithms that search in the entire space and the space of orders. Finally, we employ the hand-crafting framework to construct models for the task of diagnosis in a ''real-life'' medical domain, dementia diagnosis. We collect real dementia data from clinical practice, and we apply the data-driven algorithms developed to assess the concordance between the reference models developed by hand and the models derived from real clinical data.
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34

Rinke, Caitlin. "Selective Multivariate Applications in Forensic Science." Doctoral diss., University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5459.

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A 2009 report published by the National Research Council addressed the need for improvements in the field of forensic science. In the report emphasis was placed on the need for more rigorous scientific analysis within many forensic science disciplines and for established limitations and determination of error rates from statistical analysis. This research focused on multivariate statistical techniques for the analysis of spectral data obtained for multiple forensic applications which include samples from: automobile float glasses and paints, bones, metal transfers, ignitable liquids and fire debris, and organic compounds including explosives. The statistical techniques were used for two types of data analysis: classification and discrimination. Statistical methods including linear discriminant analysis and a novel soft classification method were used to provide classification of forensic samples based on a compiled library. The novel soft classification method combined three statistical steps: Principal Component Analysis (PCA), Target Factor Analysis (TFA), and Bayesian Decision Theory (BDT) to provide classification based on posterior probabilities of class membership. The posterior probabilities provide a statistical probability of classification which can aid a forensic analyst in reaching a conclusion. The second analytical approach applied nonparametric methods to provide the means for discrimination between samples. Nonparametric methods are performed as hypothesis test and do not assume normal distribution of the analytical figures of merit. The nonparametric permutation test was applied to forensic applications to determine the similarity between two samples and provide discrimination rates. Both the classification method and discrimination method were applied to data acquired from multiple instrumental methods. The instrumental methods included: Laser Induced-Breakdown Spectroscopy (LIBS), Fourier Transform Infrared Spectroscopy (FTIR), Raman spectroscopy, and Gas Chromatography-Mass Spectrometry (GC-MS). Some of these instrumental methods are currently applied to forensic applications, such as GC-MS for the analysis of ignitable liquid and fire debris samples; while others provide new instrumental methods to areas within forensic science which currently lack instrumental analysis techniques, such as LIBS for the analysis of metal transfers. The combination of the instrumental techniques and multivariate statistical techniques is investigated in new approaches to forensic applications in this research to assist in improving the field of forensic science.
Ph.D.
Doctorate
Chemistry
Sciences
Chemistry
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35

Reynolds, Toby J. "Bayesian modelling of integrated data and its application to seabird populations." Thesis, University of St Andrews, 2010. http://hdl.handle.net/10023/1635.

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Integrated data analyses are becoming increasingly popular in studies of wild animal populations where two or more separate sources of data contain information about common parameters. Here we develop an integrated population model using abundance and demographic data from a study of common guillemots (Uria aalge) on the Isle of May, southeast Scotland. A state-space model for the count data is supplemented by three demographic time series (productivity and two mark-recapture-recovery (MRR)), enabling the estimation of prebreeder emigration rate - a parameter for which there is no direct observational data, and which is unidentifiable in the separate analysis of MRR data. A Bayesian approach using MCMC provides a flexible and powerful analysis framework. This model is extended to provide predictions of future population trajectories. Adopting random effects models for the survival and productivity parameters, we implement the MCMC algorithm to obtain a posterior sample of the underlying process means and variances (and population sizes) within the study period. Given this sample, we predict future demographic parameters, which in turn allows us to predict future population sizes and obtain the corresponding posterior distribution. Under the assumption that recent, unfavourable conditions persist in the future, we obtain a posterior probability of 70% that there is a population decline of >25% over a 10-year period. Lastly, using MRR data we test for spatial, temporal and age-related correlations in guillemot survival among three widely separated Scottish colonies that have varying overlap in nonbreeding distribution. We show that survival is highly correlated over time for colonies/age classes sharing wintering areas, and essentially uncorrelated for those with separate wintering areas. These results strongly suggest that one or more aspects of winter environment are responsible for spatiotemporal variation in survival of British guillemots, and provide insight into the factors driving multi-population dynamics of the species.
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36

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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37

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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38

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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39

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

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40

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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41

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

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42

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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43

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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44

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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45

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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46

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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47

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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48

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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49

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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50

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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