Doing Bayesian Data Analysis - 1st Edition - ISBN: 9780123814852, 9780123814869

Doing Bayesian Data Analysis

1st Edition

A Tutorial Introduction with R

Authors: John Kruschke
eBook ISBN: 9780123814869
Imprint: Academic Press
Published Date: 27th October 2010
Page Count: 672
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There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and ‘rusty’ calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs.The textbook bridges the students from their undergraduate training into modern Bayesian methods.

Key Features

  • Accessible, including the basics of essential concepts of probability and random sampling
  • Examples with R programming language and BUGS software
  • Comprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis).
  • Coverage of experiment planning
  • R and BUGS computer programming code on website
  • Exercises have explicit purposes and guidelines for accomplishment


First-year Graduate Students and Advanced Undergraduate Students in Statistics, Psychology, Cognitive Science, Social Sciences, Clinical Sciences and Consumer Sciences in Business.

Table of Contents

1.) This Book’s Organization: Read Me First!

1.1 Real People Can Read This Book

1.2 Prerequisites

1.3 The Organization of This Book

1.3.1 What Are the Essential Chapters?

1.3.2 Where’s the Equivalent of Traditional Test X in This Book

1.4 Gimme Feedback (Be Polite)

1.5 Acknowledgments

Part 1.) The Basics: Parameters, Probability, Bayes’ Rule, and R

2.) Introduction: Models We Believe In

2.1 Models of Observations and Models of Beliefs

2.1.1 Prior and Posterior Beliefs

2.2 Three Goals for Inference from Data

2.2.1 Estimation of Parameter Values

2.2.2 Prediction of Data Values

2.2.3 Model Comparison

2.3 The R Programming Language

2.3.1 Getting and Installing R

2.3.2 Invoking R and Using the Command Line

2.3.3 A Simple Example of R in Action

2.3.4 Getting Help in R

2.3.5 Programming in R

2.4 Exercises

3.) What Is This Stuff Called Probability?

3.1 The Set of All Possible Events

3.1.1 Coin Flips: Why You Should Care

3.2 Probability: Outside or Inside the Head

3.2.1 Outside the Head: Long-Run Relative Frequency

3.2.2 Inside the Head: Subjective Belief

3.2.3 Probabilities Assign Numbers to Possibilities

3.3 Probability Distributions

3.3.1 Discrete Distributions: Probability Mass

3.3.2 Continuous Distributions: Rendezvous with Density

3.3.3 Mean and Variance of a Distribution

3.3.4 Variance as Uncertainty in Beliefs

3.3.5 Highest Density Interval (HDI)

3.4 Two-Way Distributions

3.4.1 Marginal Probability

3.4.2 Conditional Probability

3.4.3 Independence of Attributes

3.5 R Code

3.5.1 R Code for Figure 3.1

3.5.2 R Code for Figure 3.3

3.6 Exercises

4.) Bayes’ Rule

4.1 Bayes’ Rule

4.1.1 Derived from Definitions of Conditional Probability

4.1.2 Intuited from a Two-Way Discrete Table

4.1.3 The Denominator as an Integral over Continuous Values

4.2 Applied to Models and Data

4.2.1 Data Order Invariance

4.2.2 An Example with Coin Flipping

4.3 The Three Goals of Inference

4.3.1 Estimation of Parameter Values

4.3.2 Prediction of Data Values

4.3.3 Model Comparison

4.3.4 Why Bayesian Inference Can Be Difficult

4.3.5 Bayesian Reasoning in Everyday Life

4.4 R Code

4.4.1 R Code for Figure 4.1

4.5 Exercises

Part 2.) All the Fundamentals Applied to Inferring a Binomial Proportion

5.) Inferring a Binomial Proportion via Exact Mathematical Analysis

5.1 The Likelihood Function: Bernoulli Distribution

5.2 A Description of Beliefs: The Beta Distribution

5.2.1 Specifying a Beta Prior

5.2.2 The Posterior Beta

5.3 Three Inferential Goals

5.3.1 Estimating the Binomial Proportion

5.3.2 Predicting Data

5.3.3 Model Comparison

5.4 Summary: How to Do Bayesian Inference

5.5 R Code

5.5.1 R Code for Figure 5.2

5.6 Exercises

6.) Inferring a Binomial Proportion via Grid Approximation

6.1 Bayes’ Rule for Discrete Values of 0

6.2 Discretizing a Continuous Prior Density

6.2.1 Examples Using Discretized Priors

6.3 Estimation

6.4 Prediction of Subsequent Data

6.5 Model Comparison

6.6 Summary

6.7 R Code

6.7.1 R Code for Figure 6.2 and the Like

6.8 Exercises

7.) Inferring a Binomial Proportion via the Metropolis Algorithm

7.1 A Simple Case of the Metropolis Algorithm

7.1.1 A Politician Stumbles on the Metropolis Algorithm

7.1.2 A Random Walk

7.1.3 General Properties of a Random Walk

7.1.4 Why We Care

7.1.5 Why It Works

7.2 The Metropolis Algorithm More Generally

7.2.1 ""Burn-in,"" Efficiency, and Convergence

7.2.2 Terminology: Markov Chain Monte Carlo

7.3 From the Sampled Posterior to the Three Goals

7.3.1 Estimation

7.3.2 Prediction

7.3.3 Model Comparison: Estimation of p(D)

7.4 MCMC in BUGS

7.4.1 Parameter Estimation with BUGS

7.4.2 BUGS for Prediction

7.4.3 BUGS for Model Comparison

7.5 Conclusion

7.6 R Code

7.6.1 R Code for a Home-Grown Metropolis Algorithm

7.7 Exercises

8.) Inferring Two Binomial Proportions via Gibbs Sampling

8.1 Prior, Likelihood, and Posterior for Two Proportions

8.2 The Posterior via Exact Formal Analysis

8.3 The Posterior via Grid Approximation

8.4 The Posterior via Markov Chain Monte Carlo

8.4.1 Metropolis Algorithm

8.4.2 Gibbs Sampling

8.5 Doing It with BUGS

8.5.1 Sampling the Prior in BUGS

8.6 How Different Are the Underlying Biases?

8.7 Summary

8.8 R Code

8.8.1 R Code for Grid Approximation (Figures 8. and 8.2)

8.8.2 R Code for Metropolis Sampler (Figure 8.3)

8.8.3 R Code for BUGS Sampler (Figure 8.6)

8.8.4 R Code for Plotting a Posterior Histogram

8.9 Exercises

9.) Bernoulli Likelihood with Hierarchical Prior

9.1 A Single Coin from a Single Mint

9.2 Multiple Coins from a Single Mint

9.2.1 Posterior via Grid Approximation

9.2.2 Posterior via Monte Carlo Sampling

9.2.3 Outliers and Shrinkage of Individual Estimates

9.2.4 Case Study: Therapeutic Touch

9.2.5 Number of Coins and Flips per Coin

9.3 Multiple Coins from Multiple Mints

9.3.1 Independent Mints

9.3.2 Dependent Mints

9.3.3 Individual Differences and Meta-Analysis

9.4 Summary

9.5 R Code

9.5.1 Code for Analysis of Therapeutic-Touch Experiment

9.5.2 Code for Analysis of Filtration-Condensation Experiment

9.6 Exercises

10.) Hierarchical Modeling and Model Comparison

10.1 Model Comparison as Hierarchical Modeling

10.2 Model Comparison in BUGS

10.2.1 A Simple Example

10.2.2 A Realistic Example with ""Pseudopriors""

10.2.3 Some Practical Advice When Using Transdimensional MCMC with Pseudopriors

10.3 Model Comparison and Nested Models

10.4 Review of Hierarchical Framework for Model Comparison

10.4.1 Comparing Methods for MCMC Model Comparison

10.4.2 Summary and Caveats

10.5 Exercises

11.) Null Hypothesis Significance Testing

11.1 NHST for the Bias of a Coin

11.1.1 When the Experimenter Intends to Fix N

11.1.2 When the Experimenter Intends to Fix z

11.1.3 Soul Searching

11.1.4 Bayesian Analysis

11.2 Prior Knowledge about the Coin

11.2.1 NHST Analysis

11.2.2 Bayesian Analysis

11.3 Confidence Interval and Highest Density Interval

11.3.1 NHST Confidence Interval

11.3.2 Bayesian HDI

11.4 Multiple Comparisons

11.4.1 NHST Correction for Experimentwise Error

11.4.2 Just One Bayesian Posterior No Matter How You Look at It

11.4.3 How Bayesian Analysis Mitigates False Alarms

11.5 What a Sampling Distribution Is Good For

11.5.1 Planning an Experiment

11.5.2 Exploring Model Predictions (Posterior Predictive Check)

11.6 Exercises

12.) Bayesian Approaches to Testing a Point (""Null"") Hypothesis

12.1 The Estimation (Single Prior) Approach

12.1.1 Is a Null Value of a Parameter among the Credible Values?

12.1.2 Is a Null Value of a Difference among the Credible Values?

12.1.3 Region of Practical Equivalence (ROPE)

12.2 The Model-Comparison (Two-Prior) Approach

12.2.1 Are the Biases of Two Coins Equal?

12.2.2 Are Different Groups Equal?

12.3 Estimation or Model Comparison?

12.3.1 What Is the Probability That the Null Value Is True?

12.3.2 Recommendations

12.4 R Code

12.4.1 R Code for Figure 12.5

12.5 Exercises

13.) Goals, Power, and Sample Size

13.1 The Will to Power

13.1.1 Goals and Obstacles

13.1.2 Power

13.1.3 Sample Size

13.1.4 Other Expressions of Goals

13.2 Sample Size for a Single Coin

13.2.1 When the Goal Is to Exclude a Null Value

13.2.2 When the Goal Is Precision

13.3 Sample Size for Multiple Mints

13.4 Power: Prospective, Retrospective, and Replication

13.4.1 Power Analysis Requires Verisimilitude of Simulated Data

13.5 The Importance of Planning

13.6 R Code

13.6.1 Sample Size for a Single Coin

13.6.2 Power and Sample Size for Multiple Mints

13.7 Exercises

Part 3.) Applied to the Generalized Linear Model

14.) Overview of the Generalized Linear Model

14.1 The Generalized Linear Model (GLM)

14.1.2 Scale Types: Metric, Ordinal, Nominal

14.1.3 Linear Function of a Single Metric Predictor

14.1.4 Additive Combination of Metric Predictors

14.1.5 Nonadditive Interaction of Metric Predictors

14.1.6 Nominal Predictors

14.1.7 Linking Combined Predictors to the Predicted

14.1.8 Probabilistic Prediction

14.1.9 Formal Expression of the GLM

14.1.10 Two or More Nominal Variables Predicting Frequency

14.2 Cases of the GLM

14.3 Exercises

15.) Metric Predicted Variable on a Single Group

15.1 Estimating the Mean and Precision of a Normal Likelihood

15.1.1 Solution by Mathematical Analysis

15.1.2 Approximation by MCMC in BUGS

15.1.3 Outliers and Robust Estimation: The t Distribution

15.1.4 When the Data Are Non-normal: Transformations

15.2 Repeated Measures and Individual Differences

15.2.1 Hierarchical Model

15.2.2 Implementation in BUGS

15.3 Summary

15.4 R Code

15.4.1 Estimating the Mean and Precision of a Normal Likelihood

15.4.2 Repeated Measures: Normal Across and Normal Within

15.5 Exercises

16.) Metric Predicted Variable with One Metric Predictor

16.1 Simple Linear Regression

16.1.1 The Hierarchical Model and BUGS Code

16.1.2 The Posterior: How Big Is the Slope?

16.1.3 Posterior Prediction

16.2 Outliers and Robust Regression

16.3 Simple Linear Regression with Repeated Measures

16.4 Summary

16.5 R Code

16.5.1 Data Generator for Height and Weight

16.5.2 BRugs: Robust Linear Regression

16.5.3 BRugs: Simple Linear Regression with Repeated Measures

16.6 Exercises

17.) Metric Predicted Variable with Multiple Metric Predictors

17.1 Multiple Linear Regression

17.1.1 The Perils of Correlated Predictors

17.1.2 The Model and BUGS Program

17.1.3 The Posterior: How Big Are the Slopes?

17.1.4 Posterior Prediction

17.2 Hyperpriors and Shrinkage of Regression Coefficients

17.2.1 Informative Priors, Sparse Data, and Correlated Predictors

17.3 Multiplicative Interaction of Metric Predictors

17.3.1 The Hierarchical Model and BUGS Code

17.3.2 Interpreting the Posterior

17.4 Which Predictors Should Be Included?

17.5 R Code

17.5.1 Multiple Linear Regression

17.5.2 Multiple Linear Regression with Hyperprior on Coefficients

17.6 Exercises

18.) Metric Predicted Variable with One Nominal Predictor

18.1 Bayesian Oneway ANOVA

18.1.1 The Hierarchical Prior

18.1.2 Doing It with R and BUGS

18.1.3 A Worked Example

18.2 Multiple Comparisons

18.3 Two-Group Bayesian ANOVA and the NHST t Test

18.4 R Code

18.4.1 Bayesian Oneway ANOVA

18.5 Exercises

19.) Metric Predicted Variable with Multiple Nominal Predictors

19.1 Bayesian Multifactor ANOVA

19.1.2 The Hierarchical Prior

19.1.3 An Example in R and BUGS

19.1.4 Interpreting the Posterior

19.1.5 Noncrossover Interactions, Rescaling, and Homogeneous Variances

19.2 Repeated Measures, a.k.a. Within-Subject Designs

19.2.1 Why Use a Within-Subject Design? And Why Not?

19.3 R Code

19.3.1 Bayesian Two-Factor ANOVA

19.4 Exercises

20.) Dichotomous Predicted Variable

20.1 Logistic Regression

20.1.1 The Model

20.1.2 Doing It in R and BUGS

20.1.3 Interpreting the Posterior

20.1.4 Perils of Correlated Predictors

20.1.5 When There Are Few 1’s in the Data

20.1.6 Hyperprior Across Regression Coefficient

20.2 Interaction of Predictors in Logistic Regression

20.3 Logistic ANOVA

20.3.1 Within-Subject Designs

20.4 Summary

20.5 R Code

20.5.1 Logistic Regression Code

20.5.2 Logistic ANOVA Code

20.6 Exercises

21.) Ordinal Predicted Variable

21.1 Ordinal Probit Regression

21.1.1 What the Data Look Like

21.1.2 The Mapping from Metric x to Ordinal y

21.1.3 The Parameters and Their Priors

21.1.4 Standardizing for MCMC Efficiency

21.1.5 Posterior Prediction

21.2 Some Examples

21.2.1 Why Are Some Thresholds Outside the Data?

21.3 Interaction

21.4 Relation to Linear and Logistic Regression

21.5 R Code

21.6 Exercises

22.) Contingency Table Analysis

22.1 Poisson Exponential ANOVA

22.1.1 What the Data Look Like

22.1.2 The Exponential Link Function

22.1.3 The Poisson Likelihood

22.1.4 The Parameters and the Hierarchical Prior

22.2 Examples

22.2.1 Credible Intervals on Cell Probabilities

22.3 Log Linear Models for Contingency Tables

22.4 R Code for the Poisson Exponential Model

22.5 Exercises

23.) Tools in the Trunk

23.1 Reporting a Bayesian Analysis

23.1.1 Essential Points

23.1.2 Optional Points

23.1.3 Helpful Points

23.2 MCMC Burn-in and Thinning

23.3 Functions for Approximating Highest Density Intervals

23.3.1 R Code for Computing HDI of a Grid Approximation

23.3.2 R Code for Computing HDI of an MCMC Sample

23.3.3 R Code for Computing HDI of a Function

23.4 Reparameterization of Probability Distributions

23.4.1 Examples

23.4.2 Reparameterization of Two Parameters




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About the Author

John Kruschke

John K. Kruschke is Professor of Psychological and Brain Sciences, and Adjunct Professor of Statistics, at Indiana University in Bloomington, Indiana, USA. He is eight-time winner of Teaching Excellence Recognition Awards from Indiana University. He won the Troland Research Award from the National Academy of Sciences (USA), and the Remak Distinguished Scholar Award from Indiana University. He has been on the editorial boards of various scientific journals, including Psychological Review, the Journal of Experimental Psychology: General, and the Journal of Mathematical Psychology, among others.

After attending the Summer Science Program as a high school student and considering a career in astronomy, Kruschke earned a bachelor's degree in mathematics (with high distinction in general scholarship) from the University of California at Berkeley. As an undergraduate, Kruschke taught self-designed tutoring sessions for many math courses at the Student Learning Center. During graduate school he attended the 1988 Connectionist Models Summer School, and earned a doctorate in psychology also from U.C. Berkeley. He joined the faculty of Indiana University in 1989. Professor Kruschke's publications can be found at his Google Scholar page. His current research interests focus on moral psychology.

Professor Kruschke taught traditional statistical methods for many years until reaching a point, circa 2003, when he could no longer teach corrections for multiple comparisons with a clear conscience. The perils of p values provoked him to find a better way, and after only several thousand hours of relentless effort, the 1st and 2nd editions of Doing Bayesian Data Analysis emerged.

Affiliations and Expertise

Indiana University, Bloomington, USA


"This book is head-and-shoulders better than the others I've seen.  I'm using it myself right now.  Here's what's good about it: •It builds from very simple foundations. •Math is minimized.  No proofs. •From start to finish, everything is demonstrated through R programs. •It helps you learn Empirical Bayesian methods from every angle…"--Exploring Possibility Space blog, March 12, 2014