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Markov Decision Processes – Discrete Stochastic Dynamic Programming: Wiley Series in Probability and Statistics

Autor ML Puterman
en Limba Engleză Paperback – 24 feb 2005
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "This text is unique in bringing together so many results hitherto found only in part in other texts and papers. . . . The text is fairly self-contained, inclusive of some basic mathematical results needed, and provides a rich diet of examples, applications, and exercises. The bibliographical material at the end of each chapter is excellent, not only from a historical perspective, but because it is valuable for researchers in acquiring a good perspective of the MDP research potential." -Zentralblatt fur Mathematik ". . . it is of great value to advanced-level students, researchers, and professional practitioners of this field to have now a complete volume (with more than 600 pages) devoted to this topic. . . . Markov Decision Processes: Discrete Stochastic Dynamic Programming represents an up-to-date, unified, and rigorous treatment of theoretical and computational aspects of discrete-time Markov decision processes." -Journal of the American Statistical Association
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Specificații

ISBN-13: 9780471727828
ISBN-10: 0471727822
Pagini: 684
Dimensiuni: 156 x 234 x 36 mm
Greutate: 0.94 kg
Ediția:2nd ed.
Editura: Wiley
Seria Wiley Series in Probability and Statistics

Locul publicării:Hoboken, United States

Public țintă

Researchers in Statistics, Mathematics, Operations Research, Management Science, Control Engineering, Economics and Mathematical Ecology, Students.

Cuprins

Preface. 1. Introduction. 2. Model Formulation. 3. Examples. 4. Finite-Horizon Markov Decision Processes. 5. Infinite-Horizon Models: Foundations. 6. Discounted Markov Decision Problems. 7. The Expected Total-Reward. Criterion. 8. Average Reward and Related Criteria. 9. The Average Reward Criterion-Multichain and Communicating Models. 10. Sensitive Discount Optimality. 11. Continuous-Time Models. Afterword. Notation. Appendix A. Markov Chains. Appendix B. Semicontinuous Functions. Appendix C. Normed Linear Spaces. Appendix D. Linear Programming. Bibliography. Index.

Notă biografică

Martin L. Puterman, PhD, is Advisory Board Professor of Operations and Director of the Centre for Operations Excellence at The University of British Columbia in Vancouver, Canada.

Descriere

This book is an up-to-date, unified and rigorous treatment of theoretical, computational and applied research on Markov decision process models. The concentration of the book is on infinite-horizon discrete-time models, and it also discusses arbitrary state spaces, finite-horizon and continuous-time discrete-state models.