Utility-Based Learning from Data

"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; Executive Director, Masters in Math Finance, New York University; and Risk Magazine Quant of the Year 2003

"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 comprehesive 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

This book provides a pedagogical, self-contained discussion of probability estimation methods via a coherent approach from the viewpoint of a decision maker who acts in an uncertain environment. This approach is motivated by the idea that probabilistic models are usually not learned for their own sake; rather, they are used to make decisions. By taking this point of view, the book sheds light on and generalizes some popular statistical learning approaches, connecting ideas from information theory, statistics, and finance. It strikes a balance between rigor and intuition, conveying the main ideas to as wide an possible.