Preface to revised edition; Notation; 1. Introduction; I. Stochastic Models and Bayesian Filtering: 2. Stochastic state space model; 3. Optimal filtering; 4. Algorithms for maximum likelihood parameter estimation; 5. Multi-agent sensing: social learning and data incest; 6. Nonparametric Bayesian inference; II. POMDPs: Models and Applications: 7. Fully observed Markov decision processes; 8. Partially observed Markov decision processes; 9. POMDPs in controlled sensing and sensor scheduling; III. POMDP Structural Results: 10. Structural results for Markov decision processes; 11. Structural results for optimal filters; 12. Monotonicity of value function for POMDPs; 13. Structural results for stopping-time POMDPs; 14. Stopping-Time POMDPs for quickest detection; 15. Myopic policy bounds for POMDPs and sensitivity to model parameters; IV. Stochastic Gradient Algorithms and Reinforcement Learning: 16. Stochastic optimization and gradient estimation; 17. Reinforcement learning; 18. Stochastic gradient algorithms: convergence analysis; 19. Discrete stochastic optimization; V. Inverse Reinforcement Learning: 20. Revealed preferences for inverse reinforcement learning; 21. Bayesian inverse reinforcement learning; Appendix A. Short primer on stochastic stimulation; Appendix B. Continuous-time HMM filters; Appendix C. Discrete-time Martingales; Appendix D. Markov processes; Appendix E. Some limit theorems in statistics; Appendix F. Summary of POMDP algorithms; Bibliography; Index.
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