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).

"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

• I: BackgroundChapter 1: Probabilistic InformaticsChapter 2: Probability BasicsChapter 3: Statistics BasicsChapter 4: Genetics BasicsII: Bayesian NetworksChapter 5: Foundations of Bayesian NetworksChapter 6: Further Properties of Bayesian NetworksChapter 7: Learning Bayesian Network ParametersChapter 8: Learning Bayesian Network StructureIII: Bioinformatics ApplicationsChapter 9: Nonmolecular Evolutionary GeneticsChapter 10: Molecular Evolutionary GeneticsChapter 11: Molecular PhylogeneticsChapter 12: Analyzing Gene Expression DataChapter 13: Genetic Linkage AnalysisBibliographyIndex

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).

"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

• I: BackgroundChapter 1: Probabilistic InformaticsChapter 2: Probability BasicsChapter 3: Statistics BasicsChapter 4: Genetics BasicsII: Bayesian NetworksChapter 5: Foundations of Bayesian NetworksChapter 6: Further Properties of Bayesian NetworksChapter 7: Learning Bayesian Network ParametersChapter 8: Learning Bayesian Network StructureIII: Bioinformatics ApplicationsChapter 9: Nonmolecular Evolutionary GeneticsChapter 10: Molecular Evolutionary GeneticsChapter 11: Molecular PhylogeneticsChapter 12: Analyzing Gene Expression DataChapter 13: Genetic Linkage AnalysisBibliographyIndex