Markov Decision Processes – Discrete Stochastic Dynamic Programming: Wiley Series in Probability and Statistics
Autor ML Putermanen Limba Engleză Paperback – 24 feb 2005
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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
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.