Chapman and Hall/CRC
288 pages | 53 B/W Illus.
This book will describe how to use models that explain the probabilistic characteristics of a time series while the Bayesian approach will provide inferences about those probabilistic characteristics.
1. Introduction. 2. Bayesian Inference : The prior, posterior and predictive distributions. 3. Plot Trends , Seasonal Variation and Decomposition of a Series. 4. Autocorrelation, Partial Correlation, and Cross Correlation. 5. Bayesian Data Analysis for Some Fundamental Time Series. 6. Bayesian Regression Analysis with Time Series Errors. 7. Bayesian Methods for Stationary Models 8. An Analysis for Non-Stationary Models. 9. Bayesian Spectrum Analysis. 10. System Identification from a Bayesian Perspective. 11. Multivariate Models. 12. Dynamic Linear Models for Time Series. 13. Bayesian Posterior Distributions for Non-Linear Models.14. Bilinear Models and Threshold Autoregressive Processes. 15. Miscellaneous Topics in Time Series.