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 34 for “"Bayes factor"”.
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Bayesian model selection with applications to radio astronomy
… of two main parts, both of which focus on Bayesian methods and the problem of model selection in particular. The first part investigates a new approach to computing the Bayes factor for model selection without needing to compute the Bayesian evidence, while the second part shows, through an …
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Genetic Predictors of Metabolic Side Effects of Diuretic Therapy
… strategies to identify and quantify genetic factors that contribute to the development of adverse metabolic effects due to thiazide diuretic treatment. I performed a genome-wide association study (GWAS) and meta-analysis of the change in fasting plasma glucose and triglycerides in response to …
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Predictive Alternatives in Bayesian Model Selection
… this family of criteria is that it subsumes the Bayes' factor as a special case and produces an infinite family of criteria that are asymptotically equivalent to the Bayes' factor. In this manner, the criteria can be modified to achieve certain goals in small samples while maintaining asymptotic …
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A new evidence measure for Bayesian Inference
… to build a measure of evidence that covers, in a Bayesian context, the role that the p-value has played in the frequentist setting. A prominent example is the decision test based on the Bayes Factor. Worth to mention it is also the e-value, another Bayesian evidence measure on which the Full …
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Bayesian Approach Dealing with Mixture Model Problems
… Global Maximum Regions, and the second topic is Bayesian Model Selection for Nonlinear Mixed Effects Model. In the first topic, we consider a finite mixture model, which is used to fit the data from heterogeneous populations for many applications. An Expectation Maximization (EM) algorithm and …
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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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Detection of Latent Heteroscedasticity and Group-Based Regression Effects in Linear Models via Bayesian Model Selection
… predictor frequently interact with the ungrouped factor. We extend the notion of a "latent grouping factor'' to linear models in general. The proposed work allows researchers to determine whether an apparent grouping of the levels of a categorical predictor reveals a plausible hidden structure …
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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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Three papers on belief updating and its applications
The normative foundation (axioms) of Bayesian belief updating has long been established in the literature of decision science. However, psychology and experiments suggest that while rational decision making is ideal, it is rarely achievable for ordinary people. Therefore, it is important to explore …
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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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A quantitative framework For large-scale model estimation and discrimination In systems biology
… estimation and model discrimination. We use Bayesian and Monte Carlo methods to recover the full probability distributions of free parameters (initial protein concentrations and rate constants) for mass action models of receptor-mediated cell death. The width of the individual parameter …
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Bayesian Variable Selection and Inference for Nonparametric Kernel Machine and Functional Models
… and functional data. In the first study, the Bayesian variable selection method is developed under a generalized fused multi-kernel machine regression. This method can apply to continuous/binary/ordered categorical response variables. We demonstrate the advantage of our method using …
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Bayesian Nonparametric Models and Tests for Association in Survival Data
… The formulation of a test statistic that is a Bayes factor constructed from independent marginalized Polya tree priors, where the Polya tree centering distributions are Gaussian with parameters estimated from the data; as the test statistic is very fast to compute, p-values can be obtained …
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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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Advances in the Use of Finite-Set Statistics for Multitarget Tracking
… application that poses several challenges due to factors, such as, clutter/environmental noise, joint target and sensor state dependent measurement uncertainty, target-measurement association ambiguity, and sub-optimal sensor placement. The specific application that we consider is that of an …
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DETECTION AND INFERENCE IN GRAVITATIONAL WAVE ASTRONOMY
… and neutron star-black hole binaries. We use Bayesian inference to place upper limits on the rate of coalescence of these binaries. We use developments made in the PyCBC search pipeline during Advanced LIGO and Virgo’s second observing run to re-analyze Advanced LIGO’s first observing run and …
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Bayesian Multilevel-multiclass Graphical Model
… from the mixture distributions by evaluating the Bayes factor and learn the network structures by fitting a novel neighborhood selection algorithm. This approach is able to identify the class membership and to reveal network structures for multilevel variables simultaneously. Unlike most existing …
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Phylogenetic and developmental studies into the evolution of an insect novelty
… a multigene dataset and running a number of Bayesian phylogenetic analyses to investigate the effects of analysing the data under different models of evolution. In addition, Bayes factor hypothesis tests addressing the position of the insects within the Pancrustacea are described. The rest of …
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The Impact of a Digital Intervention on Perceived Stress, Resiliency, Social Support, and Intention to Leave Among Newly Licensed Nurses: A Randomized Controlled Trial
… of social support. At the end of week three, a Bayes Factor (BF) between 0.33 to 0.10 revealed substantial evidence to support there is a difference between the groups. At the end of week six, a BF between 0.03 to 0.01 revealed very strong evidence to support there is a difference between the …
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Penalised regression for high-dimensional data: an empirical investigation and improvements via ensemble learning
… systematically consider the influence of various factors (for example, sample size and sparsity) on method performance. We focus on three related goals --- prediction, variable selection and variable ranking --- and consider six widely used methods. The results are supported by a semi-synthetic …
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