Utility-Based Learning from Data

Utility-Based Learning from Data

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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.show more

Product details

  • Hardback | 417 pages
  • 162.56 x 238.76 x 25.4mm | 725.74g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • 34 black & white illustrations, 12 black & white tables
  • 1584886226
  • 9781584886228
  • 1,869,156

Review quote

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) conferencesshow more

About Craig Friedman

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.show more

Table of contents

Introduction Notions from Utility Theory Model Performance Measurement Model Estimation The Viewpoint of This Book Organization of This Book Examples Mathematical Preliminaries Some Probabilistic Concepts Convex Optimization Entropy and Relative Entropy The Horse Race The Basic Idea of an Investor in a Horse Race The Expected Wealth Growth Rate The Kelly Investor Entropy and Wealth Growth Rate The Conditional Horse Race Elements of Utility Theory Beginnings: The St. Petersburg Paradox Axiomatic Approach Risk Aversion Some Popular Utility Functions Field Studies Our Assumptions The Horse Race and Utility The Discrete Unconditional Horse Races Discrete Conditional Horse Races Continuous Unconditional Horse Races Continuous Conditional Horse Races Select Methods for Measuring Model Performance Rank-Based Methods for Two-State Models Likelihood Performance Measurement via Loss Function A Utility-Based Approach to Information Theory Interpreting Entropy and Relative Entropy in the Discrete Horse Race Context (U,O)-Entropy and Relative (U,O)-Entropy for Discrete Unconditional Probabilities Conditional (U,O)-Entropy and Conditional Relative (U,O)-Entropy for Discrete Probabilities U-Entropy for Discrete Unconditional Probabilities Utility-Based Model Performance Measurement Utility-Based Performance Measures for Discrete Probability Models Revisiting the Likelihood Ratio Utility-Based Performance Measures for Discrete Conditional Probability Models Utility-Based Performance Measures for Probability Density Models Utility-Based Performance Measures for Conditional Probability Density Models Monetary Value of a Model Upgrade Some Proofs Select Methods for Estimating Probabilistic Models Classical Parametric Methods Regularized Maximum Likelihood Inference Bayesian Inference Minimum Relative Entropy (MRE) Methods A Utility-Based Approach to Probability Estimation Discrete Probability Models Conditional Density Models Probability Estimation via Relative U-Entropy Minimization Expressing the Data Constraints in Purely Economic Terms Some Proofs Extensions Model Performance Measures and MRE for Leveraged Investors Model Performance Measures and MRE for Investors in Incomplete Markets Utility-Based Performance Measures for Regression Models Select Applications Three Credit Risk Models The Gail Breast Cancer Model A Text Classification Model References Index Exercises appear at the end of most chapters.show more

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