Utility-Based Learning from Data / Edition 1

by Craig Friedman, Sven Sandow

Overview

Utility-Based Learning from Data 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. Specifically, the authors adopt the point of view of a decision maker who

(i) operates in an uncertain environment where the consequences of every possible outcome are explicitly monetized,
(ii) bases his decisions on a probabilistic model, and
(iii) builds and assesses his models accordingly.

These assumptions are naturally expressed in the language of utility theory, which is well known from finance and decision theory. 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 audience as possible.

Product Details

ISBN-13:	9780367452322
Publisher:	CRC Press
Publication date:	11/25/2019
Series:	Chapman & Hall/CRC Machine Learning & Pattern Recognition
Pages:	417
Product dimensions:	6.12(w) x 9.19(h) x (d)

About the Author

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.

Table of Contents

Introduction. Mathematical Preliminaries. The Horse Race. Elements of Utility Theory. The Horse Race and Utility. Select Methods for Measuring Model Performance. A Utility-Based Approach to Information Theory. Utility-Based Model Performance Measurement. Select Methods for Estimating Probabilistic Models. A Utility-Based Approach to Probability Estimation. Extensions. Select Applications. References. Index.

What People are Saying About This

From the Publisher

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

Editorial Reviews

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

From the Publisher