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 8 of 8 for “"Spike-and-slab Priors"”.
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Efficient Sparse Bayesian Learning using Spike-and-Slab Priors
… of a statistical model: good predictive power and interpretability. In a Bayesian setting, sparse learning methods invoke sparsity inducing priors to explicitly encode this tradeoff in a principled manner.
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Bayesian regularized quantile mixed models for longitudinal studies
… in the presence of data heterogeneity and long tailed distributions of the disease phenotype is challenging, especially in the context of high dimensional regressions. Here, we aim at developing novel Bayesian regularized quantile mixed effect models to tackle these challenges. In the …
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Novel methods for early phase clinical trials
… clinical trials are conducted with limited time and patient resources. Despite design restrictions, patient safety must be prioritised and trial conclusions must be accurate; maximising a promising treatment’s chance of success in later largescale, long-term trials. Increasing the efficiency of …
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Low-rank graphical models and Bayesian inference in the statistical analysis of noisy neural data
… the second part we develop methods to quantify and measure information loss in analysis of neuronal spike train data due to two types of noise, making use of the ideas developed in the first part. Information about neuronal identity or temporal resolution may be lost during spike detection and …
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Change point detection for high dimensional data and valid inference for Bayesian linear models
… for high dimensional change point detection and inference for Bayesian linear models. In the first project, we propose a change point detection method testing mean shift for high dimensional observations with unknown heteroscedasticity. The proposed tests target a dense alternative and a wild …
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Bayesian variable selection in high dimensional censored regression models
… with high-dimensional data while being able to handle censoring. We focus on developing scalable algorithms for variable selection problem in a high-dimensional censored regression model that can handle gene expression data with hundreds of thousands of features. We propose an EM-like iterative …
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Bayesian regularization for graphical models and variants: Theory and algorithms
This Dissertation was approved for publication on 2019-04-19 at 09:57.