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 3048 for “"Bayesian."”.
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Topics in Bayesian sample size determination and Bayesian model selection.
This dissertation contains three topics using the Bayesian paradigm for statistical inference. The first topic is related to Bayesian sample size determination with a misclassified prevalence variable when two possibly dependent diagnostic tests are used for estimation. After accounting for the …
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Applied Bayesian Networks
<p>A Bayesian Network is a stochastic graphical model that can be used to maintain and propagate conditional probability tables among its nodes. Here, we use a Bayesian Network to model results from a numerical riverine model. We develop an discretization optimization algorithm that improves …
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Bayesian cluster validation
We propose a novel framework based on Bayesian principles for validating clusterings and present efficient algorithms for use with centroid or exemplar based clustering solutions. Our framework treats the data as fixed and introduces perturbations into the clustering procedure. In our algorithms, …
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Bayesian bilevel optimization
… the multi-objective acquisition function in the Bayesian optimization process and use the benefit of multi-objective optimization literature for using different search strategies at the upper-level search. After, the multi-objective bilevel optimization problems are investigated and a Bayesian …
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Truncated Bayesian nonparametrics
… to likewise increase over time. Priors from Bayesian nonparametrics are well-suited to this modeling challenge: they generate a countably infinite number of underlying traits, which allows the number of expressed traits to both be random and to grow with the dataset size. We also require …
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Function-Space Bayesian Learning: from Gaussian Processes to Bayesian Deep Learning
Bayesian methods provide a general and principled framework to quantify and update beliefs based on prior knowledge and observed evidence. This thesis presents my works about function-space Bayesian learning, whose priors and posteriors are specified and computed over the function-space. This …
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Bayesian Measurement Error Modeling
<p>A mixture measurement error model built upon skew normal distributions and normal distributions is developed to evaluate various impacts of measurement errors to parameter inferences in logistic regressions. Data generated from survey questionnaires are usually error contaminated. We consider …
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Essays on Bayesian Macroeconometrics
… and hours. I take the model to the data using Bayesian methods. The fluctuations caused by expectations changes from the downturn risk shock account for substantial output variations at business cycle frequencies and hours fluctuations at medium run frequencies. The extracted time-varying …
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Bayesian Adjustment for Multiplicity
<p>This thesis is about Bayesian approaches for handling multiplicity. It considers three main kinds of multiple-testing scenarios: tests of exchangeable experimental units, tests for variable inclusion in linear regresson models, and tests for conditional independence in jointly normal vectors. …
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Bayesian Inference in Regression
Made available in DSpace on 2014-12-13T18:21:45Z (GMT). No. of bitstreams: 1 7709067.pdf: 7574042 bytes, checksum: 92b2c4bc545c6d4ae67a5fa75e6ee06f (MD5) Previous issue date: 1976
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Robustness in Bayesian Inference
Made available in DSpace on 2014-12-13T18:22:18Z (GMT). No. of bitstreams: 1 8009105.pdf: 3493399 bytes, checksum: 88facf4ec77a9a0c95fec3b9fb79bb15 (MD5) Previous issue date: 1979
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Bayesian quantile linear regression
… However not much work has been done under the Bayesian framework. The most challenging problem for Bayesian quantile regression is that the likelihood is usually not available unless a certain distribution for the error is assumed. In this dissertation, we propose two Bayesian quantile …
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Bayesian Latent Class Models
… In this paper, several LCMs are developed in Bayesian framework to address new challenges in different applications. The first work is about the MR image segmentation. For MR images, we usually need to simultaneously segment multiple images, which are believed to have similar segmentation …
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Bayesian attributed network sampling
… unfamiliarity in each sampling step and it uses Bayesian approach to asses the familiarity of neighboring nodes with respect to the current sample.
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Nonparametric Bayesian behavior modeling
… To overcome these obstacles, this thesis takes a Bayesian approach and applies a Dirichlet process (DP) prior over behaviors, which uses experience to reduce the likelihood of over-fitting or under-fitting the model complexity. Additionally, the DP maintains a probability mass associated with a …
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Hierarchical Bayesian Dataset Selection
… selection algorithm that utilizes hierarchical Bayesian modeling, designed for collaborative data-sharing ecosystems. The proposed method efficiently decomposes the contributions of dataset groups and individual datasets to local model performance using Bayesian updates with small data samples. …
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Bayesian Applications in Financial Econometrics
This thesis consists of three chapters in Bayesian financial econometrics. The three chapters apply both Bayesian nonparametric and parametric methods to financial market and macroeconomic time series. Chapter 1 extends popular discrete time short-rate models to include Markov switching of infinite …
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Bayesian treed distributed lag models
… dissertation, we develop novel formulations of Bayesian additive regression trees that allow for estimating a DLM. First, we propose treed distributed lag nonlinear models to estimate the association between weekly maternal exposure to air pollution and a birth outcome when the exposure-response …
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Bayesian generalized additive model selection
… non-linear or zero on the mean response. We use Bayesian model selection paradigms and group least absolute shrinkage and selection operator (LASSO) priors. Two types of priors are explored for the sparse fits. The first, Laplace-Zero and Grouped Lasso-Zero priors, is applied to Gaussian and …
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