Probabilistic Methods for Bioinformatics: With an Introduction to Bayesian Networks - Softcover

Neapolitan, Richard E.

9780323165464: Probabilistic Methods for Bioinformatics: With an Introduction to Bayesian Networks

Softcover

ISBN 10: 032316546X ISBN 13: 9780323165464

Publisher: Morgan Kaufmann, 2009

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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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Review

"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

From the Back Cover

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

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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