The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability distributions. When used in conjunction with specialized informatics, possibilities of real-world applications are achieved. Probabilistic Methods for BioInformatics explains the application of probability and statistics, in particular Bayesian networks, to genetics. This book provides background material on probability, statistics, and genetics, and then moves on to discuss Bayesian networks and applications to bioinformatics.
Rather than getting bogged down in proofs and algorithms, probabilistic methods used for biological information and Bayesian networks are explained in an accessible way using applications and case studies. The many useful applications of Bayesian networks that have been developed in the past 10 years are discussed. Forming a review of all the significant work in the field that will arguably become the most prevalent method in biological data analysis.
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Table of Contents
I: Background
Chapter 1: Probabilistic Informatics
Chapter 2: Probability Basics
Chapter 3: Statistics Basics
Chapter 4: Genetics Basics
II: Bayesian Networks
Chapter 5: Foundations of Bayesian Networks
Chapter 6: Further Properties of Bayesian Networks
Chapter 7: Learning Bayesian Network Parameters
Chapter 8: Learning Bayesian Network Structure
III: Bioinformatics Applications
Chapter 9: Nonmolecular Evolutionary Genetics
Chapter 10: Molecular Evolutionary Genetics
Chapter 11: Molecular Phylogenetics
Chapter 12: Analyzing Gene Expression Data
Chapter 13: Genetic Linkage Analysis
Bibliography
Index
