Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/43742
Title: Variational Approximation for Complex Regression Models
Authors: TAN SIEW LI, LINDA
Keywords: Generalized linear mixed models, Mixture models, Heteroscedastic models, Variational Bayes, Stochastic approximation, Reparametrization techniques
Issue Date: 2-Jul-2013
Source: TAN SIEW LI, LINDA (2013-07-02). Variational Approximation for Complex Regression Models. ScholarBank@NUS Repository.
Abstract: The trend towards collecting large datasets has resulted in the need for more flexible models and fast computational approximations. My thesis reflects these themes by considering some very flexible regression models and developing fast variational approximation methods for fitting them under a Bayesian framework. Models considered include mixtures of heteroscedastic regression models, mixtures of linear mixed models and generalized linear mixed models. The advantages of variational methods as compared to MCMC methods are illustrated in model fitting, model selection and model criticism. In addition, we show that the use of reparametrization techniques such as hierarchical centering and partially noncentered parametrizations, which have been used to accelerate MCMC and EM algorithms for hierarchical models, can lead to improved convergence in variational algorithms as well. Finally, we demonstrate how stochastic approximation can be combined with variational methods to improve the accuracy of posterior approximations and make variational inference a viable approach for large datasets.
URI: http://scholarbank.nus.edu.sg/handle/10635/43742
Appears in Collections:Ph.D Theses (Open)

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