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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 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.