1.Introduction
2.Main definitions and notations
3.Filtering and smoothing recursions
4.Advanced topics in smoothing
5.Applications of smoothing
6.Monte carlo methods
7.Sequential monte carlo methods
8.Advanced topics in sequential monte carlo
9.Analysis of aequential monte carlo methods
10.maximum likelihood inference. Part I (Optimization through exact smoothing)
11.maximum likelihood inference. Part II (Monte carlon optimization)
12.Satistical properties of the maximum likelihood estimator
13.Fully bayesian approaches
14.Elements of markof cain theory
15.An information-theoric perspective onorder estimation
Appendix A. Conditioning
Appendix B.Linear perdication
Appendix C.Notations
1. Introduction2. Main Definitions and Notations3. Filtering and Smoothing Recursions4. Advanced Topics in Smoothing5. Applications of Smoothing6. Monte Carlo Methods7. Sequential Monte Carlo Methods8. Advanced Topics in Sequential Monte Carlo9. Advanced Topics in Sequential Monte Carlo10. Advanced Topics in Sequential Monte Carlo11. Maximum Likelihood Inference, Part II:Monte Carlo Optimization12. Statistical Properties of the MaximumLikelihood Estimator13. Fully Bayesian Approaches14. Elements of Markov Chain Theory15. An Information-Theoretic Perspective onOrder Estimation
