Filling a gap in current Bayesian theory, Statistical Inference: An Integrated Bayesian/Likelihood Approach presents a unified Bayesian treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new solutions to difficult model comparison problems and offers direct

chapter 1|20 pages

Theories of Statistical Inference

chapter 2|48 pages

The Integrated Bayes/Likelihood Approach

chapter 3|22 pages

t-Tests and Normal Variance Tests

chapter 4|42 pages

Unified Analysis of Finite Populations

chapter 5|10 pages

Regression and Analysis of Variance

chapter 6|26 pages

Binomial and Multinomial Data

chapter 7|28 pages

Goodness of Fit and Model Diagnostics

chapter 8|32 pages

Complex Models