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 “"Bayesian Mixture Models"”.
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Bayesian model-based clustering of multi-source data
… of data generated across multiple sources. Bayesian mixture models and their extensions are effective tools for partition inference in this setting as we can use these to describe and infer the relationship between different sources. I consider applying such methods to two cases of …
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Statistical methodology motivated by problems in genetics
… 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 meaningful interpretation of …
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Statistical methods for multi-omic data integration
… variable selection and building supervised models, while integrating multiple ’omic datasets of different type. It has been recently shown that applying classical logistic regression with elastic-net penalty to these datasets can lead to poor results. Therefore, we suggest a two-step …
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Statistical modeling of heterogeneous data
… in Chapter 1. In recent years non-parametric Bayesian mixture models have attracted increasing attention in the clustering literature, which is closely related with our work. So we review the Mixture of Dirichlet Process Model in Chapter 2. The main dissertation body consists of three generic …
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BLINDED EVALUATIONS OF EFFECT SIZES IN CLINICAL TRIALS: COMPARISONS BETWEEN BAYESIAN AND EM ANALYSES
… about ordering the null hypotheses. We developed Bayesian approaches that enable us to order secondary null hypotheses. These approaches are based on posterior estimation of signal-to-noise ratios. We demonstrate with simulation studies that our Bayesian algorithms perform better than existing EM …
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Robust Bayesian Anomaly Detection Methods for Large Scale Sensor Systems
… outlying sensor anomalies. We propose two Bayesian mixture model approaches that utilize heavy-tailed Cauchy assumptions. First, we propose a Robust Bayesian Regression, which utilizes a scale-mixture model to induce a Cauchy regression. Second, we extend elements of the Robust Bayesian …