Resources

You’re welcome to browse the resources below. Filter by clicking on type, difficulty level, or tags.

Filters

Toggle tags Clear filters

Level Beginner Advanced Expert

Tags

Blog Documentation Exercises Guidelines Julia Math-heavy Python R Stan Tutorial Video lecture Workflow

Beginner

Bayesian Analysis Reporting Guidelines

Kruschke (2021), Nature Human Behaviour.

Guidelines

Beginner

brms: Bayesian regression models using Stan

An R package for multilevel models using R's formula syntax, compiled to Stan.

R Documentation

Beginner

Regression and Other Stories

Gelman, Hill, and Vehtari's applied introduction to regression modelling.

R Exercises

Beginner

Statistical Rethinking

Richard McElreath's book and accompanying video lectures.

R Video lecture Exercises

Advanced

A Tutorial on Hidden Markov Models using Stan

Tutorial written by Luis Damiano (2017).

Tutorial Stan

Advanced

Bayesian Workflow

Gelman et al. (2020), arXiv.

Guidelines Workflow

Advanced

Martin Modrák's blog

My favorite post is Thank you: Statistics as a journey.

Stan R Blog

Advanced

Mattias Villani's resources

A book-in-progress and interactive widgets (see the bottom of the website for links).

Guidelines

Advanced

Prior Knowledge Elicitation: The Past, Present, and Future

Mikkola et al. (2024), Bayesian Analysis.

Guidelines Workflow

Advanced

PyMC

A probabilistic programming language in Python

Python Documentation Tutorial

Advanced

Statistical Thinking

Frank Harrell's blog about statistics.

Blog

Advanced

The Stan language

Learn about the most established probabilistic programming language nowadays.

Stan Documentation Tutorial

Advanced

Turing.jl

Probably the most established way to write probabilistic models in Julia.

Julia Documentation Tutorial

Expert

Michael Betancourt's resources

In-depth writing about statistical modelling in Stan.

Stan R Blog Math-heavy

Expert

Pattern Recognition and Machine Learning

An in-depth textbook discussing many advanced Bayesian algorithms.

Math-heavy