Short tutorial as part of KogWis2016, taught by Michael Franke and Fabian Dablander.

## motivation

Bayesian approaches to statistical inference are often portrayed as the new cool kid in town and heralded as superior to classical techniques. Naturally, the hype is also perceived critically. This course is meant to critically introduce the Bayesian approach in a nutshell. Participants who are as of yet unfamiliar with it will receive enough information to form an opinion and to know where to obtain more information that suits their needs. Those who are familiar with the main ideas can benefit from a concise rundown of the most important recent developments. In particular, this course will do two things: (i) on the conceptual level, we provide an overview of the main ideas, advantages, and challenges of Bayesian data analysis, in direct comparison to classical approaches; (ii) on a practical level, we give an executive summary of some of the most recent and convenient tools for hands-on Bayesian data analysis.

## prerequisites

We do not presuppose any particular knowledge or skills, but familiarity with basic statistical concepts will help to see the bigger picture more clearly.

## Resources

Here is a compilation of texts on Bayesian data analysis, and here is a summary of some handy tools and software.

## schedule

#### session 1: theory

slides 1

• $$p$$-values & null-hypothesis significance testing
• Bayes rule for data analysis
• things to do with data and models:
• parameter estimation
• credible intervals (a.k.a. highest density intervals)
• region of practical equivalence
• model comparison
• information criteria
• Bayes factors
• Savage-Dickey method
• comparison of methods for null-hypothesis testing

#### session 2: practice

slides 2

• Bayesian inference for complex models
• statistical models as directed acyclic graphs
• hierarchical models
• Sampling based approaches to estimation
• Metropolis-Hastings
• Gibbs sampling
• Hamiltonian MC
• tools for BDA:
• JAGS
• Stan
• WebPPL
• JASP
• rstanarm
• Bayesian cognitive modeling