Probabilistic Methods for Bioinformatics

Informationen zum Autor Richard E. Neapolitan is professor and Chair of Computer Science at Northeastern Illinois University. He has previously written four books including the seminal 1990 Bayesian network text Probabilistic Reasoning in Expert Systems. More recently, he wrote the 2004 text Learning Bayesian Networks, the textbook Foundations of Algorithms, which has been translated to three languages and is one of the most widely-used algorithms texts world-wide, and the 2007 text Probabilistic Methods for Financial and Marketing Informatics (Morgan Kaufmann Publishers). Klappentext The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability distributions. This text explains the application of probability and statistics! in particular Bayesian networks! to genetics. Inhaltsverzeichnis 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

Autorentext
Richard E. Neapolitan is professor and Chair of Computer Science at Northeastern Illinois University. He has previously written four books including the seminal 1990 Bayesian network text Probabilistic Reasoning in Expert Systems. More recently, he wrote the 2004 text Learning Bayesian Networks, the textbook Foundations of Algorithms, which has been translated to three languages and is one of the most widely-used algorithms texts world-wide, and the 2007 text Probabilistic Methods for Financial and Marketing Informatics (Morgan Kaufmann Publishers).

Klappentext

The Bayesian network is one of the most important architectures for representing and reasoning with multivariate probability distributions. This text explains the application of probability and statistics, in particular Bayesian networks, to genetics.

Zusammenfassung
"This book provides background material on probability, statistics, and genetics, and then moves on to discuss Bayesian networks and applications to bioinformatics.probabilistic methods used for biological information and Bayesian networks are explained in an accessible way using applications and case studies." --Zentralblatt MATH 1284-1

Inhalt

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