computational Bayesian methods using brms in R

Dates

14-18 June 2021

This course will be held online in response to the coronavirus outbreak

Overview

In recent years, Bayesian methods have come to be widely adopted in all areas of science. This is in large part due to the development of sophisticated software for probabilisic programming; a recent example is the astonishing computing capability afforded by the language Stan (mc-stan.org). However, the underlying theory needed to use this software sensibly is often inaccessible because end-users don't necessarily have the statistical and mathematical background to read the primary textbooks (such as Gelman et al's classic Bayesian data analysis, 3rd edition). This course provides a relatively accessible and technically non-demanding introduction to the basic workflow for fitting different kinds of linear models using a powerful front-end R package for Stan called brms.

 

Prerequisites

We assume familiarity with R. Participants will benefit most if they have previously fit linear models and linear mixed models (using lme4) in R, in any scientific domain. No knowledge of calculus or linear algebra is assumed, but basic school level mathematics knowledge is assumed (this will be quickly revisited in class).

 

 

Outcomes

After completing this course, the participants will

 

1. have become familiar with the foundations of Bayesian inference

 

2. be able to fit a range of multiple regression models and hierarchical models for normally distributed data, for log-normal, and binomially distributed data.

 

3. be able to communicate the results of a Bayesian analysis

 

4. know how to select priors for their models using prior predictive checks

 

5. know how to assess the descriptive accuracy of a model using posterior predictive checks.

 

 

As background reading, we recommend:

 

- The course will follow the first five chapters of An Introduction to Bayesian Data Analysis for Cognitive Science.

Participants will benefit from skimming the text beforehand: https://vasishth.github.io/bayescogsci/book/

 

- Other good beginner books for Bayesian analysis are McElreath’s Statistical Rethinking and Kruschke’s Doing Bayesian Data Analysis.

 

 

Outline

Monday- 09:30- 17:00

 Foundations of Bayesian inference

 

 

Foundations of Bayesian inference (Chapters 1 and 2 of An Introduction to Bayesian Data Analysis for Cognitive Science)

 

- Review of probability theory and Bayes-Price-Laplace's rule

 

- Probability distributions

 

- Analytical Bayes: Beta-Binomial

 

COst overview

Package 1

 

 

 

 

 

480 €


Cancellation Policy:

 

 

 

> 30  days before the start date = 30% cancellation fee

 

< 30 days before the start date= No Refund.

 

 

 

Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.