Resources

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

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Level

Bayesian Analysis Reporting Guidelines

Kruschke (2021), Nature Human Behaviour.

Beginner

brms: Bayesian regression models using Stan

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

Beginner

Cheatsheet for common distributions

An overview of the most commonly used probability distributions.

Beginner

Regression and Other Stories

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

Beginner

Statistical Rethinking

Richard McElreath's book and accompanying video lectures.

Intermediate

A Tutorial on Hidden Markov Models using Stan

Tutorial written by Luis Damiano (2017).

Intermediate

Bayesian Workflow

Gelman et al. (2020), arXiv.

Intermediate

Cheatsheet for advanced distributions

An overview of more advanced probability distributions and extensions.

Intermediate

Martin Modrák's blog

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

Intermediate

Mattias Villani's resources

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

Intermediate

Prior Knowledge Elicitation: The Past, Present, and Future

Mikkola et al. (2024), Bayesian Analysis.

Intermediate

PyMC

A probabilistic programming language in Python

Intermediate

Statistical Thinking

Frank Harrell's blog about statistics.

Intermediate

The Stan language

Learn about the most established probabilistic programming language nowadays.

Intermediate

Turing.jl

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

Advanced

Michael Betancourt's resources

In-depth writing about statistical modelling in Stan.

Advanced

Pattern Recognition and Machine Learning

An in-depth textbook discussing many advanced Bayesian algorithms.