Probabilistic Methods for Bioinformatics

Richard E Neapolitan

ISBN 10: 0123704766 / ISBN 13: 9780123704764

Published by Elsevier Science Apr 2009, 2009

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -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. Unique coverage of probabilistic reasoning methods applied to bioinformatics data--those methods that are likely to become the standard analysis tools for bioinformatics. Shares insights about when and why probabilistic methods can and cannot be used effectively; Complete review of Bayesian networks and probabilistic methods with a practical approach. 424 pp. Englisch.

Seller Inventory # 9780123704764

Bibliographic Details

• Title: Probabilistic Methods for Bioinformatics
• Publisher: Elsevier Science Apr 2009
• Publication Date: 2009
• Language: English
• ISBN 10: 0123704766
• ISBN 13: 9780123704764
• Binding: Buch
• Condition: Neu

About this title

Synopsis

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 of this title are as mentioned below. It 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. It shares insights about when and why probabilistic methods can and cannot be used effectively. It provides complete review of Bayesian networks and probabilistic methods for those IT professionals new to probabilistic informatics. It presents unique coverage of probabilistic reasoning methods applied to bioinformatics data - those methods that are likely to become the standard analysis tools for bioinformatics. It offers complete review of Bayesian networks and probabilistic methods with a practical approach.

About 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).