State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms.
The book illustrates all the fundamental steps needed to use dynamic linear models in practice,...
State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After ...
