The book is an ideal reference for statisticians and scientists who work with time series data. It provides an excellent resource for teaching a course on Bayesian analysis using state space models for time series.
Key Features:
- Introduction and overview of R-INLA for time series analysis.
- Gaussian and non-Gaussian state space models for time series.
- State space models for time series with exogenous predictors.
- Hierarchical models for a potentially large set of time series.
- Dynamic modelling of stochastic volatility and spatio-temporal dependence.
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