Front cover; Contents; Preface; Chapter 1: Theories of Statistical Inference; Chapter 2: The Integrated Bayes/Likelihood Approach; Chapter 3: t-Tests and Normal Variance Tests; Chapter 4: Unified Analysis of Finite Populations; Chapter 5: Regression and Analysis of Variance; Chapter 6: Binomial and Multinomial Data; Chapter 7: Goodness of Fit and Model Diagnostics; Chapter 8: Complex Models; Author Index; Subject Index; Back cover
Summary
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 Bayesian counterparts of frequentist t-tests and other standard statistical methods for hypothesis testing. After an overview of the competing theories of statistical inference, the book introduces the Bayes/likelihood approach used throughout. It pre
