Average Reviews:
(More customer reviews)This book is just as great inside the cover as
the elegant cover leads you to expect.
A very ambitious book with a very broad scope.
As a Professor of Applied Mathematics and
of mathematical finance, I very much look
forward to presenting parts of this material
in the future.
Concerning the contents, citing from the introduction of
the book:"Our point of view is motivated by the notion that probabilistic models are
usually not learned for their own sake-rather, they are used to make decisions"
and "finance and decision theory provide a language in which it is
natural to express these assumptions-namely, utility theory-and formulate,
from first principals, model performance measures and the notion of optimal
and robust model performance"
and the books purpose is : " to provide a pedagogical and self-contained discussion of a select set of
methods for estimating probability distributions that can be approached
coherently from a decision-theoretic point of view"
The last sentence is extremely telling. Friedman and Sandow indeed
demonstrate in this book that, in struggling to quantify
default risk, in their daytime jobs at Standard and Poor's,
they carefully put into place their own approach, and painstakingly
tested it on read data, throughout many different economic
cycles (as far back as 2001, when I worked in Friedman's group).
In addition, after Friedman presented some of this material at
New York University's Courant Institute, Friedman and Sandow saw fit to
include a through introduction to topics which are of interest
to all economic students, such as utility theory and
minimum relative theory. And they do so in a crisp, clear and no-nonsense
manner that is rarely seen in books on economics.
A key aspect of the point of view taken in this book, is to relate
betting odds, such as in a horse race, to expected
growth of wealth.
Readers should race to the bookstore to get a
hold of this book!
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Utility-Based Learning from Data (Chapman & Hall/Crc: Machine Learning & Pattern Recognition)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 possible outcomes 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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