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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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