This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. Topics discussed are; basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way material previously published by the author, with unpublished work.
This dissertation is the result of 4 years at Utrecht University as a Ph.D-student at the Department of Information and Computing Sciences. The work presented in this thesis is mainly based on the research published in various papers during that time. However, it is not merely a bundle of research articles. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this thesis combines in a clarifying way all the issues presented in the papers with previously unpublished work. I hope that my efforts have been worthwhile!
My gratitude goes to my supervisor and co-promotor, dr. Ad Feelders. I am thankful to my promotor, prof.dr. Arno Siebes and to Jeroen De Knijf, Edwin de Jong and all the other colleagues in the Algorithmic Data Analysis group, and at the computer science department.
I would like to thank the members of the outstanding reading committee: prof.dr.ir. Linda van der Gaag, prof.dr. Richard D. Gill, prof.dr. Finn V. Jensen, prof.dr. Bert Kappen and prof.dr. Pedro Larrañaga.
The present dissertation was successfully defended on October 23, 2006 at Utrecht University. For publication in the series “Frontiers in Artificial Intelligence and Applications”, IOS Press, only minor details have been corrected and very few additions have been made.
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