Bayesian Analysis of Time Series

In many branches of science relevant observations are taken sequentially over time. Bayesian Analysis of Time Series discusses how to use models that explain the probabilistic characteristics of these time series and then utilizes the Bayesian approach to make inferences about their parameters. This is done by taking the prior information and via Bayes theorem implementing Bayesian inferences of estimation, testing hypotheses, and prediction. The methods are demonstrated using both R and WinBUGS. The R package is primarily used to generate observations from a given time series model, while the WinBUGS packages allows one to perform a posterior analysis that provides a way to determine the characteristic of the posterior distribution of the unknown parameters.

Features

Presents a comprehensive introduction to the Bayesian analysis of time series.
Gives many examples over a wide variety of fields including biology, agriculture, business, economics, sociology, and astronomy.
Contains numerous exercises at the end of each chapter many of which use R and WinBUGS.
Can be used in graduate courses in statistics and biostatistics, but is also appropriate for researchers, practitioners and consulting statisticians.