1.2 Exploring a Student Dataset

3.4 An Illustration of Bayesian Robustness: Learning About a
Normal Mean with Known Variance

5.6 The Example

6.9 Modeling Data with Cauchy Errors

7.9 Bayesian Sensitivity Analysis

9.3 Model Selection using Zellner’s g Prior

1.3 Exploring the Robustness of the t Statistic

3.5 Mixtures of Conjugate Priors

5.7 Monte Carlo Method for Computing Integrals

6.10 Analysis of the Stanford Heart Transplant Data

7.10 Posterior Predictive Model Checking

9.4 Survival Modeling

2.3 Using a Discrete Prior

3.6 A Bayesian Test of the Fairness of a Coin

5.8 Rejection Sampling

7.2 Introduction to Hierarchical Modeling

8.3 A OneSided Test of a Normal Mean

10.2 Robust Modeling

2.4 Using a Beta Prior

4.2 Normal Data with Both Parameters Unknown

5.9 Importance Sampling

7.3 Individual and Combined Estimates

8.4 A TwoSided Test of a Normal Mean

10.3 Binary Response Regression with a Probit Link

2.5 Using a Histogram Prior

4.3 A Multinomial Model

5.10 Sampling Importance Resampling

7.4 Equal Mortality Rates?

8.6 Models for Soccer Goals

10.4 Estimating a Table of Means

2.6 Prediction

4.4 A Bioassay Experiment

6.2 Introduction to Discrete Markov Chains

7.5 Modeling a Prior Belief of Exchangeability

8.7 Is a Baseball Hitter Really Streaky?

11.4 A ChangePoint Model

3.2 Normal Distribution with Known Mean but Unknown Variance

4.5 Comparing Two Proportions

6.7 Learning About a Normal Population from Grouped Data

7.7 Simulating from the Posterior

8.8 A Test of Independence in a TwoWay Contingency Table

11.5 A Robust Regression Model

3.3 Estimating a Heart Transplant Mortality Rate

5.4 A BetaBinomial Model for Overdispersion

6.8 Example of Output Analysis

7.8 Posterior Inferences

9.2 A Regression Example

11.6 Estimating Career Trajectories
