part 1 Part I: Foundations -- chapter 1 Linear Models -- chapter 2 Linear Gaussian State Space Models -- chapter 3 Beyond Linear Models -- chapter 4 Stochastic Recurrence Equations -- part 2 Part II: Markovian Models -- chapter 5 Markov Models: Construction and Definitions -- chapter 6 Stability and Convergence -- chapter 7 Sample Paths and Limit Theorems -- chapter 8 Inference for Markovian Models -- part 3 Part III: State Space and Hidden Markov Models -- chapter 9 Non-Gaussian and Nonlinear State Space Models -- chapter 10 Particle Filtering -- chapter 11 Particle Smoothing -- chapter 12 Inference for Nonlinear State Space Models -- chapter 13 Asymptotics of the MLE for NLSS.