Utility-Based Learning from Data (Chapman & Hall/CRC Machine Learning & Pattern Recognition)

Friedman, Craig; Sandow, Sven

ISBN 10: 1584886226 / ISBN 13: 9781584886228

Editore: Chapman and Hall/CRC, 2010

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Codice articolo SHUB192770

Dati bibliografici

• Titolo: Utility-Based Learning from Data (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
• Editore: Chapman and Hall/CRC
• Data di pubblicazione: 2010
• Lingua: Inglese
• ISBN 10: 1584886226
• ISBN 13: 9781584886228
• Rilegatura: Rilegato
• Condizione: nuovo

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

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.