Introduction to Bayesian Data AnalysisProf. Dr. Shravan Vasishth, Dr. Anna Laurinavichyute

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Week 0 - Initial Setup

Please install the latest versions of R and RStudio, rstan, brms, and other necessary packages in R. In order to get the most out of this course, please read the textbook chapters 1-4 (the textbook link is provided below) as the course progresses. Each chapter belongs to the corresponding week in this course.

Week 1 - Introduction

Learn the foundational ideas about random variables and probability distributions. Reading: Chapter 1 of the textbook.

Week 2 - Bayesian data analysis

Understand Bayes' rule, derive the posterior using Bayes' rule; visualize the prior, likelihood, and posterior; distinguish between the prior, likelihood, and posterior; incorporate prior knowledge into the analysis. Reading: Chapter 2.

Week 3 - Computational Bayesian data analysis

Derive the posterior through sampling; build a simple linear regression model using brms; visualize prior predictive distributions, perform sensitivity analysis and posterior predictive checks. Reading: Chapter 3.

Week 4 - Bayesian regression and hierarchical models

Perform simple linear regressions using the normal and binomial likelihoods to answer the following research questions: (i) Does attentional load affect pupil size? (ii) Does trial id affect response times? (iii) Does set size affect recall accuracy? Take a brief look-ahead at linear mixed models. Reading: Chapter 4 and up to section 5.3 of Chapter 5.

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