**Prologue **

Probability of a Defective: Binomial Data

Brass Alloy Zinc Content: Normal Data

Armadillo Hunting: Poisson Data

Abortion in Dairy Cattle: Survival Data

Ache Hunting with Age Trends

Lung Cancer Treatment: Log-Normal Regression

Survival with Random Effects: Ache Hunting

**Fundamental Ideas I**

Simple Probability Computations

Science, Priors, and Prediction

Statistical Models

Posterior Analysis

Commonly Used Distributions

**Integration versus Simulation**

Introduction

WinBUGS I: Getting Started

Method of Composition

Monte Carlo Integration

Posterior Computations in R

**Fundamental Ideas II**

Statistical Testing

Exchangeability

Likelihood Functions

Sufficient Statistics

Analysis Using Predictive Distributions

Flat Priors

Jeffreys’ Priors

Bayes Factors

Other Model Selection Criteria

Normal Approximations to Posteriors

Bayesian Consistency and Inconsistency

Hierarchical Models

Some Final Comments on Likelihoods

Identifiability and Noninformative Data

**Comparing Populations **

Inference for Proportions

Inference for Normal Populations

Inference for Rates

Sample Size Determination

Illustrations: Foundry Data

Medfly Data

Radiological Contrast Data

Reyes Syndrome Data

Corrosion Data

Diasorin Data

Ache Hunting Data

Breast Cancer Data

**Simulations **

Generating Random Samples

Traditional Monte Carlo Methods

Basics of Markov Chain Theory

Markov Chain Monte Carlo

**Basic Concepts of Regression**

Introduction

Data Notation and Format

Predictive Models: An Overview

Modeling with Linear Structures

Illustration: FEV Data

**Binomial Regression**

The Sampling Model

Binomial Regression Analysis

Model Checking

Prior Distributions

Mixed Models

Illustrations: Space Shuttle Data

Trauma Data

Onychomycosis Fungis Data

Cow Abortion Data

**Linear Regression**

The Sampling Model

Reference Priors

Conjugate Priors

Independence Priors

ANOVA

Model Diagnostics

Model Selection

Nonlinear Regression

Illustrations: FEV Data

Bank Salary Data

Diasorin Data

Coleman Report Data

Dugong Growth Data

**Correlated Data**

Introduction

Mixed Models

Multivariate Normal Models

Multivariate Normal Regression

Posterior Sampling and Missing Data

Illustrations: Interleukin Data

Sleeping Dog Data

Meta-Analysis Data

Dental Data

**Count Data **

Poisson Regression

Over-Dispersion and Mixtures of Poissons

Longitudinal Data

Illustrations: Ache Hunting Data

Textile Faults Data

Coronary Heart Disease Data

Foot and Mouth Disease Data

**Time to Event Data**

Introduction

One-Sample Models

Two-Sample Data

Plotting Survival and Hazard Functions

Illustrations: Leukemia Cancer Data

Breast Cancer Data

**Time to Event Regression **

Accelerated Failure Time Models

Proportional Hazards Modeling

Survival with Random Effects

Illustrations: Leukemia Cancer Data

Larynx Cancer Data

Cow Abortion Data

Kidney Transplant Data

Lung Cancer Data

Ache Hunting Data

**Binary Diagnostic Tests**

Basic Ideas

One Test, One Population

Two Tests, Two Populations

Prevalence Distributions

Illustrations: Coronary Artery Disease

Paratuberculosis Data

Nucleospora Salmonis Data

Ovine Progressive Pnemonia Data

**Nonparametric Models**

Flexible Density Shapes

Flexible Regression Functions

Proportional Hazards Modeling

Illustrations: Galaxy Data

ELISA Data for Johnes Disease

Fungus Data

Test Engine Data

Lung Cancer Data

**Appendix A: Matrices and Vectors**

**Appendix B: Probability**

**Appendix C: Getting Started in R**

**References**