Probabilistic Methods for Bioinformatics: with an Introduction to Bayesian Networks

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Overview

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 Financial and Marketing Informatics covers two of the most important applications of informatics and concentrates on approaches to solving realistic business problems. This book provides applications of informatics to areas such as managerial options and decision making, investment science, marketing, and data mining, concentrating on the probabilistic and decision-theoretic approaches to informatics, emphasizing the use of Bayesian networks. Rather than dwelling on rigor, algorithms, and proofs of theorems, the authors focus on problem solving and practical applications. Included in this book are ample examples and exercises throughout, as well as six chapters that each walk through pragmatic situations. In many cases, solutions are expanded, as the authors discuss their final implementation using the software package Netica. Features:

  • Unique coverage of probabilistic reasoning topics applied to business problems, including advertising, market basket analysis, venture capital decision making, operational risk measurement, bankruptcy prediction, and investment science.
  • Shares insights about when and why probabilistic methods can and cannot be used effectively.
  • Complete review of Bayesian networks and probabilistic methods for those IT professionals new to probabilistic informatics
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Editorial Reviews

From the Publisher
"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
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Product Details

  • ISBN-13: 9780123704764
  • Publisher: Elsevier Science
  • Publication date: 4/17/2009
  • Edition description: New Edition
  • Pages: 424
  • Product dimensions: 7.70 (w) x 9.30 (h) x 1.20 (d)

Meet the Author

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).

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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

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