Utility-Based Learning from Data

Front Cover
CRC Press, Apr 19, 2016 - Computers - 417 pages

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.

 

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Contents

Introduction
1
Mathematical Preliminaries
33
The Horse Race
79
Elements of Utility Theory
95
The Horse Race and Utility
111
Select Methods for Measuring Model Performance
139
A UtilityBased Approach toInformation Theory
155
UtilityBased Model Performance Measurement
181
Select Methods for Estimating Probabilistic Models
229
A UtilityBased Approach to Probability Estimation
259
Extensions
313
Select Applications
349
References
379
Back cover
391
Copyright

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About the author (2016)

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.