This book covers formulation, algorithms, and structural results of partially observed Markov decision processes, linking theory to real-world applications in controlled sensing.
This book covers formulation, algorithms, and structural results of partially observed Markov decision processes, linking theory to real-world applications in controlled sensing.
Vikram Krishnamurthy is a Professor and Canada Research Chair in Statistical Signal Processing at the University of British Columbia, Vancouver. His research contributions focus on nonlinear filtering, stochastic approximation algorithms and POMDPs. Dr Krishnamurthy is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE) and served as a distinguished lecturer for the IEEE Signal Processing Society. In 2013, he received an honorary doctorate from KTH, Royal Institute of Technology, Sweden.
Inhaltsangabe
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.
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.
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