Advances in Probabilistic Graphical Models for Optimization and Learning. Applications in Protein Modelling

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“Advances in Probabilistic Graphical Models for Optimization and Learning. Applications in Protein Modelling” by R. Santana. Ph.D. dissertation, University of the Basque Country, 2006.

Abstract

The first part focuses on the analysis of undirected graphical models. The ways in which inference and sampling methods based on undirected graphical models can be applied to optimization are discussed. The second part of the thesis introduces a number of properties of the class of Kikuchi approximation that use clique-based decompositions. An algorithm that learns this approximation from data is introduced and evaluated in different types of approximation problem. Region-based decompositions in optimization methods that use inference techniques are analyzed. The second part comprises three chapters. The third part of the thesis addresses the application of optimization algorithms based on graphical models to problems from computational biology. It starts by reviewing a number of computational protein problems. Different proposals that allow the efficient solution of some of these problems are introduced. A link to the results achieved in the first part of the thesis is presented by showing how the probabilistic models and techniques introduced can be added to obtain solutions good enough for these problems. Part four consists of only one chapter that presents the conclusions of the thesis.

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BibTeX entry:

@phdthesis{Santana:2006,
   author = {R. Santana},
   title = {Advances in Probabilistic Graphical Models for Optimization
	and Learning. {A}pplications in Protein Modelling},
   school = {University of the Basque Country},
   type = {{Ph.D.}},
   year = {2006}
}

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