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Description
Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step-by-step instructions on how to carry out Bayesian data analyses in the popular and free software R and WinBugs, as well as new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition. In particular, there are now compact high-level scripts that make it easy to run the programs on your own data sets.

The book is divided into three parts and begins with the basics: models, probability, Bayes' rule, and the R programming language. The discussion then moves to the fundamentals applied to inferring a binomial probability, before concluding with chapters on the generalized linear model. Topics include metric-predicted variable on one or two groups; metric-predicted variable with one metric predictor; metric-predicted variable with multiple metric predictors; metric-predicted variable with one nominal predictor; and metric-predicted variable with multiple nominal predictors. The exercises found in the text have explicit purposes and guidelines for accomplishment. View more >

Key Features
Accessible, including the basics of essential concepts of probability and random sampling
Examples with R programming language and JAGS 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 JAGS computer programming code on website
Exercises have explicit purposes and guidelines for accomplishment
Provides step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs



Table of Contents
Chapter 1: What´s in This Book (Read This First!)

1.1 Real people can read this book
1.2 What´s in this book
1.3 What´s new in the second edition?
1.4 Gimme feedback (Be polite)
1.5 Thank you!
Part I: The Basics: Models, Probability, Bayes' Rule, and R

Introduction

Chapter 2: Introduction: Credibility, Models, and Parameters

2.1 Bayesian inference is reallocation of credibility across possibilities
2.2 Possibilities are parameter values in descriptive models
2.3 The steps of bayesian data analysis
2.4 Exercises
Chapter 3: The R Programming Language

3.1 Get the software
3.2 A simple example of R in action
3.3 Basic commands and operators in R
3.4 Variable types
3.5 Loading and saving data
3.6 Some utility functions
3.7 Programming in R
3.8 Graphical plots: Opening and saving
3.9 Conclusion
3.10 Exercises
Chapter 4: What is This Stuff Called Probability?

4.1 The set of all possible events
4.2 Probability: Outside or inside the head
4.3 Probability distributions
4.4 Two-way distributions
4.5 Appendix: R code for figure 4.1
4.6 Exercises
Chapter 5: Bayes´ Rule

5.1 Bayes´ rule
5.2 Applied to parameters and data
5.3 Complete examples: Estimating bias in a coin
5.4 Why bayesian inference can be difficult
5.5 Appendix: R code for figures 5.1, 5.2, etc.
5.6 Exercises
Part II: All the Fundamentals Applied to Inferring a Binomial Probability

Introduction

Chapter 6: Inferring a Binomial Probability via Exact Mathematical Analysis

6.1 The likelihood function: Bernoulli distribution
6.2 A description of credibilities: The beta distribution
6.3 The posterior beta
6.4 Examples
6.5 Summary
6.6 Appendix: R code for figure 6.4
6.7 Exercises
Chapter 7: Markov Chain Monte Carlo

7.1 Approximating a distribution with a large sample
7.2 A simple case of the metropolis algorithm
7.3 The metropolis algorithm more generally
7.4 Toward gibbs sampling: Estimating two coin biases
7.5 Mcmc representativeness, accuracy, and efficiency
7.6 Summary
7.7 Exercises
Chapter 8: JAGS

8.1 Jags and its relation to R
8.2 A complete example
8.3 Simplified scripts for frequently used analyses
8.4 Example: difference of biases
8.5 Sampling from the prior distribution in jags
8.6 Probability distributions available in jags
8.7 Faster sampling with parallel processing in runjags
8.8 Tips for expanding jags models
8.9 Exercises
Chapter 9: Hierarchical Models

9.1 A single coin from a single mint
9.2 Multiple coins from a single mint
9.3 Shrinkage in hierarchical models
9.4 Speeding up jags
9.5 Extending the hierarchy: Subjects within categories
9.6 Exercises
Chapter 10: Model Comparison and Hierarchical Modeling

10.1 General formula and the bayes factor
10.2 Example: two factories of coins
10.3 Solution by MCMC
10.4 Prediction: Model averaging
10.5 Model complexity naturally accounted for
10.6 Extreme sensitivity to prior distribution
10.7 Exercises
Chapter 11: Null Hypothesis Significance Testing

11.1 Paved with good intentions
11.2 Prior knowledge
11.3 Confidence interval and highest density interval
11.4 Multiple comparisons
11.5 What a sampling distribution is good for
11.6 Exercises
Chapter 12: Bayesian Approaches to Testing a Point ("Nullö) Hypothesis

12.1 The estimation approach
12.2 The model-comparison approach
12.3 Relations of parameter estimation and model comparison
12.4. Estimation or model comparison?
12.5. Exercises
Chapter 13: Goals, Power, and Sample Size

13.1 The will to power
13.2 Computing power and sample size
13.3 Sequential testing and the goal of precision
13.4 Discussion
13.5 Exercises
Chapter 14: Stan

14.1 HMC sampling
14.2 Installing stan
14.3 A complete example
14.4 Specify models top-down in stan
14.5 Limitations and extras
14.6 Exercises
Part III: The Generalized Linear Model

Introduction

Chapter 15: Overview of the Generalized Linear Model

15.1 Types of variables
15.2 Linear combination of predictors
15.3 Linking from combined predictors to noisy predicted data
15.4 Formal expression of the GLM
15.5 Exercises
Chapter 16: Metric-Predicted Variable on One or Two Groups

16.1 Estimating the mean and standard deviation of a normal distribution
16.2 Outliers and robust estimation: The t distribution
16.3 Two groups
16.4 Other noise distributions and transforming data
16.5 Exercises
Chapter 17: Metric Predicted Variable with One Metric Predictor

17.1 Simple linear regression
17.2 Robust linear regression
17.3 Hierarchical regression on individuals within groups
17.4 Quadratic trend and weighted data
17.5 Procedure and perils for expanding a model
17.6 Exercises
Chapter 18: Metric Predicted Variable with Multiple Metric Predictors

18.1 Multiple linear regression
18.2 Multiplicative interaction of metric predictors
18.3 Shrinkage of regression coefficients
18.4 Variable selection
18.5 Exercises
Chapter 19: Metric Predicted Variable with One Nominal Predictor

19.1 Describing multiple groups of