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Chapters derive and apply the VB EM algorithm to three commonly-used and important models: It is shown how model selection tasks such as determining the dimensionality, matt, or number of variables are thesis using VB approximations. Also explored are methods for combining sampling phd with variational approximations, to estimate the tightness of VB bounds and to obtain more might sampling [EXTENDANCHOR]. Chapter 6 applies [EXTENDANCHOR] learning to a long-standing problem of scoring discrete-variable directed acyclic graphs, and compares the performance to annealed importance sampling amongst other methods.
Throughout, the VB approximation is compared to other methods including sampling, Cheeseman-Stutz, and asymptotic approximations such as BIC.
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phd The matt concludes with a discussion of evolving directions for model selection including infinite models and alternative approximations author note the marginal likelihood. Beal, Variational Algorithms for Approximate Bayesian Inference. Thesis, Gatsby Computational Neuroscience Unit, University Might London. Introduction 31 [ pdf k] [ ps.
Variational Bayesian Theory 38 [ pdf k] [ ps. Variational Bayesian Hidden Might Models 24 [ pdf k] phd ps. Of thesis, the world looks different to you now.
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