Dynamic Time Series Models using R-INLA

<p><b><i>Dynamic Time Series Models using R-INLA: An Applied Perspective</i></b> is the outcome of a joint effort to systematically describe the use of R-INLA for analysing time series and showcasing the code and description by several examples. This book introduces the underpinnings of R-INLA and the tools needed for modelling different types of time series using an approximate Bayesian framework.</p><p>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.</p><p>Key Features:</p><ul> <li>Introduction and overview of R-INLA for time series analysis.</li> <li>Gaussian and non-Gaussian state space models for time series.</li> <li>State space models for time series with exogenous predictors.</li> <li><em>Hierarchical models for a potentially large set of time series. </em></li> <li>Dynamic modelling of stochastic volatility and spatio-temporal dependence.</li> </ul>