1. Introduction Part I: The linear state space model 2. Local level model 3. Linear Gaussian 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 Gaussian 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 Subject Index
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