Probabilistic Methods for Financial and Marketing Informatics

1st Edition

Print ISBN: 9780123704771
eBook ISBN: 9780080555676
Imprint: Morgan Kaufmann
Published Date: 19th March 2007
Page Count: 432
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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

Key Features

  • 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.


This book is for all R&D professionals and students who are involved with industrial informatics, that is, applying the methodologies of computer science and engineering to business or industry information. This includes Computer Science and other professionals in the data management and data mining field whose interests are business and marketing information in general, and who want to apply AI and probabilistic methods to their problems--in order to better predict how well a product or service will do in a particular market, for instance. Typical fields where this technology is used is in advertising, venture capital decision making, operational risk measurement in any industry, credit scoring, and investment science.

Table 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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© Morgan Kaufmann 2007
Morgan Kaufmann
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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