Tesi: "Bayesian statistical decision theory"

1

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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2

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

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3

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 Optimal design for changepoint problems has only been addressed in an unpublished doctoral thesis, and in only one journal article, which was in a frequentist setting.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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4

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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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

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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7

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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8

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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9

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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10

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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11

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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12

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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13

Van, Gael Jurgen. "Bayesian nonparametric hidden Markov models." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610196.

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14

Lau, Wai Kwong. "Bayesian nonparametric methods for some econometric problems /." View abstract or full-text, 2005. http://library.ust.hk/cgi/db/thesis.pl?ISMT%202005%20LAU.

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15

Sarpong, Abeam Danso. "Tolerance intervals for variance component models using a Bayesian simulation procedure." Thesis, Nelson Mandela Metropolitan University, 2013. http://hdl.handle.net/10948/d1021025.

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Abstract (sommario):

The estimation of variance components serves as an integral part of the evaluation of variation, and is of interest and required in a variety of applications (Hugo, 2012). Estimation of the among-group variance components is often desired for quantifying the variability and effectively understanding these measurements (Van Der Rijst, 2006). The methodology for determining Bayesian tolerance intervals for the one – way random effects model has originally been proposed by Wolfinger (1998) using both informative and non-informative prior distributions (Hugo, 2012). Wolfinger (1998) also provided relationships with frequentist methodologies. From a Bayesian point of view, it is important to investigate and compare the effect on coverage probabilities if negative variance components are either replaced by zero, or completely disregarded from the simulation process. This research presents a simulation-based approach for determining Bayesian tolerance intervals in variance component models when negative variance components are either replaced by zero, or completely disregarded from the simulation process. This approach handles different kinds of tolerance intervals in a straightforward fashion. It makes use of a computer-generated sample (Monte Carlo process) from the joint posterior distribution of the mean and variance parameters to construct a sample from other relevant posterior distributions. This research makes use of only non-informative Jeffreys‟ prior distributions and uses three Bayesian simulation methods. Comparative results of different tolerance intervals obtained using a method where negative variance components are either replaced by zero or completely disregarded from the simulation process, is investigated and discussed in this research.

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16

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

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17

馮榮錦 and Wing-kam Tony Fung. "Analysis of outliers using graphical and quasi-Bayesian methods." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1987. http://hub.hku.hk/bib/B31230842.

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18

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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19

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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20

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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21

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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22

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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23

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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24

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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25

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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26

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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27

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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28

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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29

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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30

Woodard, Roger. "Bayesian hierarchical models for hunting success rates /." free to MU campus, to others for purchase, 1999. http://wwwlib.umi.com/cr/mo/fullcit?p9951135.

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31

Oleson, Jacob J. "Bayesian spatial models for small area estimation /." free to MU campus, to others for purchase, 2002. http://wwwlib.umi.com/cr/mo/fullcit?p3052203.

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32

Puza, Borek Dalibor. "Aspects of Bayesian biostatistics." Thesis, Canberra, ACT : The Australian National University, 1994. http://hdl.handle.net/1885/140911.

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33

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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34

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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35

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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36

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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37

Delcroix, Sophie M. "Bayesian Analysis of Cancer Mortality Rates from Different Types and their Relative Occurrences." Digital WPI, 1999. https://digitalcommons.wpi.edu/etd-theses/1114.

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"We analyze mortality data from prostate, colon, lung, and all other types (called other cancer) to obtain age specific and age adjusted mortality rates for white males in the U.S. A related problem is to estimate the relative occurrences of these four types of cancer. We use Bayesian method because it permits a degree of smoothing which is needed to analyze data at a small area level and to assess the patterns. In the recent Atlas of the United States Mortality (1996) each type of cancer was analyzed individually. The difficulty in doing so is that there are many small areas with zero deaths. We conjecture that simultaneous analyses might help to overcome this problem, and at the same time to estimate the relative occurrences. We start with a Poisson model for the deaths, which produces a likelihood function that separates into two parts: a Poisson likelihood for the rates and a multinomial likelihood for the relative occurrences. These permit the use of a standard Poisson regression model on age as in Nandram, Sedransk and Pickle (1999), and the novelty is a multivariate logit model on the relative occurrences in which per capita income, the percent of people below poverty level, education (percent of people with four years of college) and two criteria pollutants, EPAPM25 and EPASO2, are used as covariates. We fitted the models using Markov chain Monte Carlo methods. We used one of the models to present maps of occurrences and rates for the four types. An alternative model did not work well because it provides the same pattern by age and disease. We found that while EPAPM25 has a negative effect on the occurrences, EPASO2 has a positive effect. Also, we found some interesting patterns associated with the geographical variations of mortality rates and the relative occurrences of the four cancer types."

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38

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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39

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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40

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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41

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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42

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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43

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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44

陳潔妍 and Kit-yin Chan. "Bayesian analysis of wandering vector models for ranking data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31214939.

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45

Chan, Kit-yin. "Bayesian analysis of wandering vector models for ranking data /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19977025.

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46

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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47

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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Abstract (sommario):

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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48

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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49

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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50

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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Tesi sul tema "Bayesian statistical decision theory"

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