Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 21 for “"Bayes Factors"”.
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A study of Population MCMC for estimating Bayes Factors over nonlinear ODE models
… of new model hypotheses. Towards that end, Bayesian methodology provides an ideal framework for tackling such challenges, and in particular offers a means of objectively comparing competing plausible models through the estimation of Bayes factors. There are, however, formidable obstacles …
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Analysis of word-order universals using Bayesian phylogenetic inference
… approach by Dunn et al. (2011) that employs the Bayesian phylogenetic inference to compute the Bayes Factors that determine whether the evolutions of a set of word-order traits in four language families are correlated or independent. In the first part of the thesis, the phylogenetic trees of the …
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Detecting episodes of star formation using Bayesian model selection.
Bayesian model comparison is a data-driven method to establish model complexity. In this dissertation we investigate its use in detecting multiple episodes of star formation from the analysis of the Spectral Energy Distribution (SED) of galaxies. This method is validated by simulating galaxy …
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Contributions to Statistical Problems Related to Microarray Data
… related to microarray data. First, we propose a Bayesian model for microarray data and use Bayes Factors to identify differentially expressed genes. Second, we study the cellular differentiation process and proposed a statistical test for detecting early differentiation genes. Third, we further …
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Bayesian estimation of stochastic volatility models with fat tails and correlated errors applied to the South African financial market
… we apply Markov Chain Monte Carlo methods in the Bayesian framework to estimate Stochastic Volatility models using South African financial market data. A single move Gibbs sampler is used to sample parameters from the posterior distribution. Volatility is used as measure of an asset's risk. It is …
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Rationality or irrationality of preferences? A quantitative test of intransitive decision heuristics
… different experiments using both frequentist and Bayesian order-constrained statistical methods. The frequentist goodness-of-fit tests show that the distance-based models with modal choice and the mixture models for all of the decision heuristics explain the participants' data fairly well for all …
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Bayesian Interaction and Association Networks From Multiple Replicates of Sparse Time-Course Data
… replicates. This is accomplished through Bayesian inference in conjunction with the Metropolis-Hastings algorithm. The Bayesian posterior probability is computed for two distinct cases. One case assumes the replicates are independent events, the other assumes the replicates are not …
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Bayesian model determination for categorical data survey
… this thesis is to present an investigation of a Bayesian approach to the analysis of categorical survey data, arising from designs including simple random sampling, finite population sampling, stratification, and cluster sampling. We focus on Bayesian methods for model selection and model …
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Bayesian Model Selection for Spatial Data and Cost-constrained Applications
Bayesian model selection is a useful tool for identifying an appropriate model class, dependence structure, and valuable predictors for a wide variety of applications. In this work we consider objective Bayesian model selection where no subjective information is available to inform priors on model …
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Bayesian Model Uncertainty and Prior Choice with Applications to Genetic Association Studies
<p>The Bayesian approach to model selection allows for uncertainty in both model specific parameters and in the models themselves. Much of the recent Bayesian model uncertainty literature has focused on defining these prior distributions in an objective manner, providing conditions under which …
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Objective bayesian variable selection for censored data
… and observations are right censored. Under a Bayesian approach, the most widely used tools are the Bayes Factors (BFs) which are, however, undefined when using improper priors. Some commonly used tools in literature, which solve the problem of indeterminacy in model selection, are the …
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Bayesian Model Selection in terms of Kullback-Leibler discrepancy
… model assessment and selection methods for Bayesian models, when we anticipate that a promising approach should be objective enough to accept, easy enough to understand, general enough to apply, simple enough to compute and coherent enough to interpret. We mainly restrict attention to the …
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Model selection in rheology: Providing a practical framework, surveying the field, and assessing the uses and limitations of BIC
… the application of simple criteria such as the Bayesian Information Criterion (BIC) may add significant value and validity to these analyses. There remains even greater opportunity in the application of more sophisticated methods such as the calculation of Bayes Factors and the formulation of …
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A psychophysical investigation of quantum cognition: An interdisciplinary synthesis
… bootstrapping, model comparison based on Bayes Factors analysis, Bayesian bootstrapping, and Bayesian parameter estimation via Markov chain Monte Carlo simulations). This multimethod approach enabled us to analytically cross-validate our experimental results, thereby increasing the …
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Testing Methods of Testing Replication Success
… sizes, using heterogeneity tests and comparing Bayes Factors across original and replication studies. The relationship between sampling variability and various study characteristics on the expected outcomes of these tests was complex. Unlike the assumption that consistency in findings confirms …
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Testing Methods of Testing Replication Success
… sizes, using heterogeneity tests and comparing Bayes Factors across original and replication studies. The relationship between sampling variability and various study characteristics on the expected outcomes of these tests was complex. Unlike the assumption that consistency in findings confirms …
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Rationality or irrationality of preferences? Quantitative tests of decision theories
… Falmagne, 1985, Silvapulle and Sen, 2005) and Bayesian (Myung et al., 2005) order-constrained, likelihood-based statistical inference methods. This thesis is one of the largest scale projects for a systematic evaluation of both transitive and intransitive decision theories. The quantitative …
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Molecular phylogenetics of the North American snake tribe Thamnophiini
… supported using gene tree constraint tests and Bayes factors, where we rejected the monophyly of both the crayfish snakes (genus Regina) and the earth snakes (genus Virginia). Progress in our understanding of molecular evolution necessitates a more thorough assessment of the phylogeny of …
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Application of statistical analysis techniques to solar and stellar phenomena
… way for making model comparisons using Bayesian statistics. In order to quantify our beliefs we use Bayes factors and information criteria such as AIC and BIC. Three simulated data-sets are analysed in order to validate the procedure and assess the effects of varying error bar size. …
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Testing transitivity using inequality-constrained inference: Transitivity, probabilistic models, and inequality-constrained inference
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2026-12-01
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