Table of Contents

Bayesian Intro

Welcome to the world of Bayesian statistics!

Getting Started

Let’s make sure you have everything you need to get off to a good start!

Lecture Slides: Intro to Bayesian Inference

These lecture slides introduce the basic concepts for Bayesian inference: Probability distributions, likelihoods, priors, posteriors, Markov chain Monte Carlo simulation…

Practical Tutorial: Model of the Mean

We will start with simple model so that we can focus on the Bayesian implementation rather than the statistical details. Our goal is to simply estimate the mean of a…

Practical Tutorial: Posteriors, Likelihoods, and Priors

To get experience designing priors, we will explore how informative priors interact with the likelihood to influence the posterior.

Practical Tutorial: A Toy MCMC Example

In this toy example, we manually calculate a Markov Chain Monte Carlo (MCMC) simulation for a coin flip process with a potentially unfair coin to demystify how MCMC works.

Practical Tutorial: Gaussian Linear Regression

Now that we can design and run a Bayesian model of the mean, let’s add a deterministic linear regression on the mean.

Lecture Slides: GLM and Hierarchical Models

These lecture slides extend on the fundamentals to include flexible modelling frameworks of generalised linear models and hierarchical models.

Practical Tutorial: Synthetic Controls

Imagine an alternate reality in which Russia’s large scale invasion of Ukraine never happened. Or where the British referendum vote had rejected Brexit. Would global…

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