Course information

Course title
Introduction to Bayesian Inference in Practice
University / Organization
Transmitting Science
Academic level
Both Msc and PhD
Course director
Course lecturer(s)
Dr Daniele Silvestro (University of Gothenburg, Sweden) Tobias Hofmann (University of Gothenburg, Sweden)
Course aim
Course overview 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 this Bayesian machinery works is 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. The instructor will outline the relevant concepts and basic theory, but the focus of the course will be to learn how to do Bayesian inference in practice. He will show 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. He 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. Participants are encouraged to think of potential applications of Bayesian inference in their research, which we will discuss and try to implement during the course.
Course contents / programme
Monday, June 11th, 2018. Introduction to probabilistic models and Bayes theorem. Tuesday, June 12th, 2018. Markov Chain Monte Carlo: how it works, how to implement it. Wednesday, June 13th, 2018. Hypothesis testing using marginal likelihoods. Thursday, June 14th, 2018. Hierarchical modelling and Bayesian shrinkage. Friday, June 15th, 2018. Modelling patterns vs modelling processes; Gibbs sampling.
Required Knowledge and preparation
Teaching methods
Course type
Consecutive days
Online course
Duration (net days)
June 11th-15th, 2018
Course fee
Direct e-mail to register
Paper submission required?
Logo of institution
front approval