The book "Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan" by John K. Kruschke is a comprehensive guide to Bayesian data analysis, suitable for both beginners and advanced users. The book is divided into three main parts: 1. **The Basics: Models, Probability, Bayes' Rule, and R**: - Introduction to Bayesian inference and its practical applications. - Overview of the R programming language, including basic commands, data handling, and graphical plots. - Concepts of probability, including distributions and Bayes' rule. - Examples and exercises to reinforce understanding. 2. **Applying Bayesian Methods to Infer a Binomial Probability**: - Exact mathematical analysis of binomial probabilities using the beta distribution. - Markov Chain Monte Carlo (MCMC) methods, including the Metropolis algorithm and Gibbs sampling. - Introduction to JAGS (a Bayesian inference tool) and its integration with R. - Hierarchical models and model comparison. - Null hypothesis significance testing and Bayesian alternatives. - Topics on goals, power, and sample size. - Overview of Stan, a high-performance Bayesian inference tool. 3. **The Generalized Linear Model**: - Overview of the generalized linear model (GLM) and its applications. - Analysis of metric-predicted variables with one or two groups, including robust estimation and outliers. - Simple and multiple linear regression, including hierarchical and robust approaches. - Analysis of nominal and dichotomous predicted variables, including logistic regression. - Ordinal and count data models, including Poisson and log-linear models. - Additional tools and resources for reporting Bayesian analyses, reparameterization, and handling censored data. The book includes numerous examples, exercises, and practical tips to help readers apply Bayesian methods to real-world data analysis.The book "Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan" by John K. Kruschke is a comprehensive guide to Bayesian data analysis, suitable for both beginners and advanced users. The book is divided into three main parts: 1. **The Basics: Models, Probability, Bayes' Rule, and R**: - Introduction to Bayesian inference and its practical applications. - Overview of the R programming language, including basic commands, data handling, and graphical plots. - Concepts of probability, including distributions and Bayes' rule. - Examples and exercises to reinforce understanding. 2. **Applying Bayesian Methods to Infer a Binomial Probability**: - Exact mathematical analysis of binomial probabilities using the beta distribution. - Markov Chain Monte Carlo (MCMC) methods, including the Metropolis algorithm and Gibbs sampling. - Introduction to JAGS (a Bayesian inference tool) and its integration with R. - Hierarchical models and model comparison. - Null hypothesis significance testing and Bayesian alternatives. - Topics on goals, power, and sample size. - Overview of Stan, a high-performance Bayesian inference tool. 3. **The Generalized Linear Model**: - Overview of the generalized linear model (GLM) and its applications. - Analysis of metric-predicted variables with one or two groups, including robust estimation and outliers. - Simple and multiple linear regression, including hierarchical and robust approaches. - Analysis of nominal and dichotomous predicted variables, including logistic regression. - Ordinal and count data models, including Poisson and log-linear models. - Additional tools and resources for reporting Bayesian analyses, reparameterization, and handling censored data. The book includes numerous examples, exercises, and practical tips to help readers apply Bayesian methods to real-world data analysis.