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 6 of 6 for “"Label switching problem"”.
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Statistical methodology motivated by problems in genetics
… First, probabilistic methods to deal with the label switching problem in Bayesian mixture models are introduced. Mixture models are used in situations where populations may consist of a number of sub-populations, or as a semi-parametric modelling tool. The label switching problem can prevent …
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Bayesian analysis for mixtures of discrete distributions with a non-parametric component
… number of components to be used and due to the label-switching problem. The use of a non-parametric distribution to model the signal component is proposed. This new methodology leads to more accurate parameter estimation, smaller classification error rate and smaller false non-discovery rate in …
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Toward Faster Methods in Bayesian Unsupervised Learning
… information in these unsupervised learning problems, such as the hierarchy among words, documents, and latent topics, one can use Bayesian probabilistic models. The application of Bayesian unsupervised learning faces three computational challenges. Firstly, existing works aim to speed up …
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Structure as simplification : transportation tools for understanding data
… intepret. This thesis proposes solutions to both problems by leveraging the theory of optimal transport and proposing efficient algorithms to solve problems in: (1) quantization, with extensions to the Wasserstein barycenter problem, and a link to the classical coreset problem; (2) natural …
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Some Model-Based and Distance-Based Clustering Methods for Characterization of Regional Ecological Stressor-Response Patterns and Regional Environmental Quality Trends
… Carlo algorithm. Two general approaches to the label-switching problem are considered, each leading to procedures that we apply in data analyses. Two applications are presented. We explore some relationships among priors with a Dirichlet distribution for class probabilities. We compare two …
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Bayesian Regression Inference Using a Normal Mixture Model
In this thesis we develop a two component mixture model to perform a Bayesian regression. We implement our model computationally using the Gibbs sampler algorithm and apply it to a dataset of differences in time measurement between two clocks. The dataset has ``good" time measurements and ``bad" …