PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Computing the stochastic complexity of simple probabilistic graphical models
Tommi Mononen
(2009) PhD thesis, University of Helsinki.

Abstract

Minimum Description Length (MDL) is an information-theoretic principle that can be used for model selection and other statistical inference tasks. There are various ways to use the principle in practice. One theoretically valid way is to use the normalized maximum likelihood (NML) criterion. Due to computational difficulties, this approach has not been used very often. This thesis presents efficient floating-point algorithms that make it possible to compute the NML for multinomial, Naive Bayes and Bayesian forest models. None of the presented algorithms rely on asymptotic analysis and with the first two model classes we also discuss how to compute exact rational number solutions.

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EPrint Type:Thesis (PhD)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:5562
Deposited By:Tommi Mononen
Deposited On:01 March 2010