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Informationen zum Autor 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). Klappentext Bayesian Networks are a form of probabilistic graphical models and they are used for modeling knowledge in many application areas, from medicine to image processing. They are particularly useful for business applications, ans * Unique coverage of probabilistic reasoning topics applied to business problems, including marketing, banking, operations management, and finance. * 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 informatics. Zusammenfassung Bayesian Networks are a form of probabilistic graphical models and they are used for modeling knowledge in many application areas! from medicine to image processing. This book provides a coverage of probabilistic reasoning topics applied to business problems! including marketing! banking! operations management! and finance. Inhaltsverzeichnis I: Informatics and Baysesian Networks; Introduction to Informatics; Basics of Probability and Statistics; Algorithms for Bayesian Networks; Decision Trees and Influence Diagrams. II: Business Informatics: Collaborative Filtering; Targeted Advertising; Market Basket Analysis; Venture Capital Decision Making; Measuring Operational Risk; Credit Scoring; Applications to Investment Science. Appendices.
List of contents
I: Informatics and Baysesian Networks; Introduction to Informatics; Basics of Probability and Statistics; Algorithms for Bayesian Networks; Decision Trees and Influence Diagrams. II: Business Informatics: Collaborative Filtering; Targeted Advertising; Market Basket Analysis; Venture Capital Decision Making; Measuring Operational Risk; Credit Scoring; Applications to Investment Science. Appendices.
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"One of the features I appreciate most is that almost all the examples refer to real-world situations, which is especially useful because it shows how to build probabilistic models for other domains." --Francisco Diez, UNED, Madrid, Spain
