Self-paced course

Introduction to Bayesian Data Analysis

Offered by Prof. Dr. Shravan Vasishth, Dr. Anna Laurinavichyute

The listed learning units belong to the course Introduction to Bayesian Data Analysis. Do you want to access all course content?

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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.

Welcome to the Course

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Preparatory R course (optional)

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Textbook to accompany the course (available online)

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General course information: Introduction to Bayesian Data Analysis

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Learning Material

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Week 1 - Introduction

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

Welcome to Week 1

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Pre-Course Survey

Survey

1.1 Discrete random variables

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1.2 Discrete random variables (The binomial)

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Binomial distribution quiz

Self-test

1.3 Continuous random variables

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1.4 Continuous random variables (example: the normal distribution)

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1.5 Continuous random variables (example: the normal distribution)

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Normal distribution quiz

Self-test

1.6 Maximum likelihood estimates

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1.7 Bivariate and multivariate distributions (Discrete case)

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Bivariate distribution quiz

Self-test

1.8 Bivariate and multivariate distributions (Continuous case)

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Learning Material

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Practical self-test for Week 1

Self-test

Homework - Week 1

Graded Test

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.

Welcome to week 2

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2.1 Bayes' Rule

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2.2 Bayes' rule in action (Binomial-Beta conjugate case)

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2.3 Bayes' rule in action (Poisson-Gamma conjugate case)

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2.4 Bayes' rule in action (Poisson-Gamma conjugate case - contd.)

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2.5 The Posterior Mean

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Selftest Week 2

Self-test

Learning Material

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Homework - Week 2

Graded Test

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.

Welcome to Week 3

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3.1 Computational Bayes

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3.2 Computational Bayes

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3.3 Computational Bayes

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3.4 Computational Bayes

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3.5 Computational Bayes

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3.6 Computational Bayes

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3.7 Computational Bayes

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3.8 Computational Bayes

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Selftest Week 3

Self-test

Learning Material

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Homework Week 3

Graded Test

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.

Welcome to Week 4

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4.1 Regression models

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4.2 Regression models

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4.3 Regression models

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4.4 Regression models

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4.5 Regression models

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4.6 Regression models

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4.7 Regression models

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4.8 Regression models (hierarchical regression)

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Selftest Week 4

Self-test

Learning Material

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Homework Week 4

Graded Test

I like, I wish

Please provide your feedback on the course.

I like, I wish - what is that?

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Post-Course Survey

Survey