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Bayesian approaches to learning from data how to untangle the travel behavior and land use relationshipsScuderi, Marco Giovanni. January 2005 (has links)
Thesis (Ph. D.)--University of Maryland, College Park, 2005. / Includes bibliographical references (p. 167-176). Also available online via the University of Maryland digital repository website (https://drum.umd.edu/).
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Modeling distributions of test scores with mixtures of beta distributions /Feng, Jingyu, January 2005 (has links) (PDF)
Project (M.S.)--Brigham Young University. Dept. of Statistics, 2005. / Includes bibliographical references (p. 51-52).
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Diagnostic tools and remedial methods for collinearity in linear regression models with spatially varying coefficientsWheeler, David C. January 2006 (has links)
Thesis (Ph. D.)--Ohio State University, 2006. / Available online via OhioLINK's ETD Center; full text release delayed at author's request until 2007 Aug 14
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Statistical learning and predictive modeling in data miningLi, Bin. January 2006 (has links)
Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 67-72).
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Identification of activation of transcription factors from microarray data /Kossenkov, Andrei. T̈ozeren, Aydin. January 2007 (has links)
Thesis (Ph. D.)--Drexel University, 2007. / Includes abstract and vita. Includes bibliographical references (leaves 103-115).
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Dependent evidence in reasoning with uncertaintyLing, Xiaoning 06 December 1990 (has links)
The problem of handling dependent evidence is an important practical issue for
applications of reasoning with uncertainty in artificial intelligence. The existing solutions
to the problem are not satisfactory because of their ad hoc nature, complexities, or
limitations.
In this dissertation, we develop a general framework that can be used for extending
the leading uncertainty calculi to allow the combining of dependent evidence. The leading
calculi are the Shafer Theory of Evidence and Odds-likelihood-ratio formulation of Bayes
Theory. This framework overcomes some of the disadvantages of existing approaches.
Dependence among evidence from dependent sources is assigned dependence
parameters which weight the shared portion of evidence. This view of dependence leads
to a Decomposition-Combination method for combining bodies of dependent evidence.
Two algorithms based on this method, one for merging, the other for pooling a sequence
of dependent evidence, are developed. An experiment in soybean disease diagnosis is
described for demonstrating the correctness and applicability of these methods in a
domain of the real world application. As a potential application of these methods, a
model of an automatic decision maker for distributed multi-expert systems is proposed.
This model is a solution to the difficult problem of non-independence of experts. / Graduation date: 1991
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Uses of Bayesian posterior modes in solving complex estimation problems in statisticsLin, Lie-fen 17 March 1992 (has links)
In Bayesian analysis, means are commonly used to
summarize Bayesian posterior distributions. Problems with
a large number of parameters often require numerical
integrations over many dimensions to obtain means. In this
dissertation, posterior modes with respect to appropriate
measures are used to summarize Bayesian posterior
distributions, using the Newton-Raphson method to locate
modes. Further inference of modes relies on the normal
approximation, using asymptotic multivariate normal
distributions to approximate posterior distributions. These
techniques are applied to two statistical estimation
problems.
First, Bayesian sequential dose selection procedures
are developed for Bioassay problems using Ramsey's prior
[28]. Two adaptive designs for Bayesian sequential dose
selection and estimation of the potency curve are given.
The relative efficiency is used to compare the adaptive
methods with other non-Bayesian methods (Spearman-Karber,
up-and-down, and Robbins-Monro) for estimating the ED50 .
Second, posterior distributions of the order of an
autoregressive (AR) model are determined following Robb's
method (1980). Wolfer's sunspot data is used as an example
to compare the estimating results with FPE, AIC, BIC, and
CIC methods. Both Robb's method and the normal
approximation for estimation of the order have full
posterior results. / Graduation date: 1992
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Bayesian methods for solving linear systemsChan, Ka Hou January 2011 (has links)
University of Macau / Faculty of Science and Technology / Department of Mathematics
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Approximation methods for efficient learning of Bayesian networksRiggelsen, Carsten. January 1900 (has links)
Thesis (Ph.D.)--Utrecht University, 2006. / Includes bibliographical references (p. [133]-137).
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Risk measures in finance and insuranceSiu, Tak-kuen. January 2001 (has links)
Thesis (Ph. D.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 192-202).
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