Partially observed Markov decision processes : from filtering to controlled sensing
By: Krishnamurthy, Vikram
Material type:
BookPublisher: Cambridge : Cambridge University Press, c2016.Description: xiii, 476 p. : ill. ; 26 cm.ISBN: 9781107134607Subject(s): Markov processes -- Textbooks | Stochastic processes -- TextbooksDDC classification: 519.2/33 Online resources: Location Map | Item type | Home library | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
| REGULAR | University of Wollongong in Dubai Main Collection | 519.233 KR PA (Browse shelf) | Available | T0011229 |
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| 519.202462 JO MI Miller and Freund's probability and statistics for engineers / | 519.202462 JO MI Miller and Freund's probability and statistics for engineers / | 519.202462 JO MI Miller and Freund's probability and statistics for engineers / | 519.233 KR PA Partially observed Markov decision processes : | 519.282 AL SI Simulation modeling and analysis with Arena / | 519.282 AL SI Simulation modeling and analysis with Arena / | 519.3 CH NU Numerical optimization with applications / |
Includes bibliographical references and index.
Preface; 1. Introduction; Part I. Stochastic Models and Bayesian Filtering: 2. Stochastic state-space models; 3. Optimal filtering; 4. Algorithms for maximum likelihood parameter estimation; 5. Multi-agent sensing: social learning and data incest; Part II. Partially Observed Markov Decision Processes. Models and Algorithms: 6. Fully observed Markov decision processes; 7. Partially observed Markov decision processes (POMDPs); 8. POMDPs in controlled sensing and sensor scheduling; Part III. Partially Observed Markov Decision Processes: 9. Structural results for Markov decision processes; 10. Structural results for optimal filters; 11. Monotonicity of value function for POMPDs; 12. Structural results for stopping time POMPDs; 13. Stopping time POMPDs for quickest change detection; 14. Myopic policy bounds for POMPDs and sensitivity to model parameters; Part IV. Stochastic Approximation and Reinforcement Learning: 15. Stochastic optimization and gradient estimation; 16. Reinforcement learning; 17. Stochastic approximation algorithms: examples; 18. Summary of algorithms for solving POMPDs; Appendix A. Short primer on stochastic simulation; Appendix B. Continuous-time HMM filters; Appendix C. Markov processes; Appendix D. Some limit theorems; Bibliography; Index.
This book covers formulation, algorithms, and structural results of partially observed Markov decision processes, linking theory to real-world applications in controlled sensing.