Introduction to Bayesian Inference in Practice

Most researchers in life sciences are exposed in their research to a multitude of methods and algorithms to test hypotheses, infer parameters, explore empirical data sets, etc. Bayesian methods have become standard practice in several fields, (e.g. phylogenetic inference, evolutionary (paleo)biology, genomics), yet understanding how these Bayesian machinery works are not always trivial.

This course is based on the assumption that the easiest way to understand the principles of Bayesian inference and the functioning of the main algorithms is to implement these methods yourself.

On this course, we will outline the relevant concepts and basic theory, but the focus of the course will be to learn how to perform Bayesian inference in practice. We will demonstrate how to implement the most common algorithms to estimate parameters based on posterior probabilities, such as Markov Chain Monte Carlo samplers, and how to build hierarchical models. We will also touch upon hypothesis testing using Bayes factors and Bayesian variable selection.

The course will take a learn-by-doing approach, in which participants will implement their own MCMCs using R or Python (templates for both languages will be provided).

After completion of the course, the participants will have gained a better understanding of how the main Bayesian methods implemented in many programs used in biological research work. Participants will also learn how to model at least basic problems using Bayesian statistics and how to implement the necessary algorithms to solve them. The aim is that, by the end of the course, each participant will have written their own MCMC – from scratch!

Participants are expected to have some knowledge of R or Python (each can choose their preferred language), but they will be guided “line-by-line” in writing their script.

Participants are encouraged to bring their own datasets and questions and we will (try to) figure them out during the course and implement scripts to analyse them in a Bayesian framework.

Places are limited to 18 participants.