Utility-Based Learning from Data by Craig Friedman

Utility-Based Learning from Data by Craig Friedman

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.

Utility-Based Learning from Data is an excellent treatment of data-driven statistics for decision-makingFriedman 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

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.

SKU	CIN1584886226VG
ISBN 13	9781584886228
ISBN 10	1584886226
Title	Utility-Based Learning from Data
Author	Craig Friedman
Series	Chapman And Hall Crc Machine Learning And Pattern Recognition
Condition	Very Good
Publisher	Taylor & Francis Inc
Year published	2010-08-12
Number of pages	418
Cover note	Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
Note	This is a used book - there is no escaping the fact it has been read by someone else and it will show signs of wear and previous use. Overall we expect it to be in very good condition, but if you are not entirely satisfied please get in touch with us