Inference in Hidden Markov Models

1. Introduction

2. Main Definitions and Notations

Part I. State Inference

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 Sequential Monte Carlo Methods

Part II. Parameter Inference

10. Maximum Likelihood Inference, Part I: Optimization Through Exact Smoothing

11. Maximum Likelihood Inference, Part II: Monte Carlo Optimization

12. Statistical Properties of the Maximum Likelihood Estimator

13. Fully Bayesian Approaches

Part III. Background and Complements

14. Elements of Markov Chain Theory

15. An Information-Theoretic Perspective on Order Estimation

DRM-restrictions

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Keywords: MATHEMATICS / Probability & Statistics / General MAT029000