1. Introduction

Part I. The linear state space model
2. Local level model
3. Linear state space models
4. Filtering, smoothing and forecasting
5. Initialisation of filter and smoother
6. Further computational aspects
7. Maximum likelihood estimation of parameters
8. Illustrations of the use of the linear model

Part II. Non-Gaussian and nonlinear state space models
9. Special cases of nonlinear and non-Gaussian models
10. Approximate filtering and smoothing
11. Importance sampling for smoothing
12. Particle filtering
13. Bayesian estimation of parameters
14. Non-Gaussian and nonlinear illustrations