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9781322612287

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Utility-Based Learning from Data is an excellent treatment of data-driven statistics for decision-making. Friedman and Sandow lucidly describe the connections between different branches of statistics and econometrics, such as utility theory, maximum entropy, and Bayesian analysis. A must-read for serious statisticians! Marco Avellaneda, Professor of Mathematics, New York University, and Risk Magazine Quant of the Year 2010 Combining insights from both theory and practice, this is a model trade book about modeling trading books. Peter Carr, Global Head of Market Modeling, Morgan Stanley, and Executive Director, Masters in Math Finance, New York University Utility-Based Learning from Data connects key ideas from utility theory with methods from statistics, machine learning, and information theory. It presents, using decision-theoretic principles, a framework for building models that can be used by decision makers. By adopting the utility-based approach, Friedman and Sandow are able to adapt models to the risk preferences of the model user, while maintaining tractability. It is a much-needed and comprehensive book, which should help put model-building for use by decision makers on more solid ground. Gregory Piatetsky-Shapiro, editor of KDnuggets.com, co-founder and past Chair of SIGKDD, and founder of the Knowledge Discovery and Data Mining (KDD) conferences

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Craig Friedman is a managing director and head of research in the Quantitative Analytics group at Standard & Poor s in New York. Dr. Friedman is also a fellow of New York University s Courant Institute of Mathematical Sciences. He is an associate editor of both the International Journal of Theoretical and Applied Finance and the Journal of Credit Risk. Sven Sandow is an executive director in risk management at Morgan Stanley in New York. Dr. Sandow is also a fellow of New York University s Courant Institute of Mathematical Sciences. He holds a Ph.D. in physics and has published articles in scientific journals on various topics in physics, finance, statistics, and machine learning. The contents of this book are Dr. Sandow s opinions and do not represent Morgan Stanley.

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