Book contents
- Frontmatter
- Contents
- 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 Applications
- Part III Partially Observed Markov Decision Processes: Structural Results
- Part IV Stochastic Approximation and Reinforcement Learning
- Appendix A Short primer on stochastic simulation
- Appendix B Continuous-time HMM filters
- Appendix C Markov processes
- Appendix D Some limit theorems
- References
- Index
4 - Algorithms for maximum likelihood parameter estimation
from Part I - Stochastic Models and Bayesian Filtering
Published online by Cambridge University Press: 05 April 2016
- Frontmatter
- Contents
- 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 Applications
- Part III Partially Observed Markov Decision Processes: Structural Results
- Part IV Stochastic Approximation and Reinforcement Learning
- Appendix A Short primer on stochastic simulation
- Appendix B Continuous-time HMM filters
- Appendix C Markov processes
- Appendix D Some limit theorems
- References
- Index
Summary
- Type
- Chapter
- Information
- Partially Observed Markov Decision ProcessesFrom Filtering to Controlled Sensing, pp. 73 - 92Publisher: Cambridge University PressPrint publication year: 2016