Description
Book SynopsisBayesian statistics uses information from past experience to infer the results of future events. With recent advances in computing power and the development of computer intensive methods for statistical estimation, Bayesian approaches to model estimation have become more feasible and popular.
Trade Review"I recommend…highly to statisticians, [and] health researchers...among others to consider keeping on their bookshelf." (
Journal of Statistical Computation and Simulation, April 2005)
"…a great book…fills a critical gap in existing literature. It is an excellent book for anyone interested in Bayesian modeling…" (Journal of the American Statistical Association, March 2005)
"It is certainly a fine choice as a supporting reference in either a first or second Bayesian methods course…” (Technometrics, May 2004)
"...has a contemporary feel, with recent developments in financial time series modelling and epidemiology included..." (Short Book Reviews, Vol 23(3), December 2003)
Table of ContentsPreface.
The Basis for, and Advantages of, Bayesian Model Estimation via Repeated Sampling.
Hierarchical Mixture Models.
Regression Models.
Analysis of Multi-Level Data.
Models for Time Series.
Analysis of Panel Data.
Models for Spatial Outcomes and Geographical Association.
Structural Equation and Latent Variable Models.
Survival and Event History Models.
Modelling and Establishing Causal Relations: Epidemiological Methods and Models.
Index.
