Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and Inference integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian t
Notation, Definitions, and Basic Inference. Traditional Time Domain Models. The Frequency Domain. Dynamic Linear Models. State-Space Time-Varying Autoregressive Models. Sequential Monte Carlo Methods for State-Space Models. Mixture Models in Time Series. Topics and Examples in Multiple Time Series. Vector AR and ARMA Models. Multivariate DLMs and Covariance Models. Indices. Bibliography.