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Introduction to Bayesian Inference in Practice


Sept 18-22, 2023

Lang EN Workshop language is English

Prof. Daniele Silvestro, UNIFR


Prof. Daniele Silvestro, UNIFR


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. Bayesian methods have become standard practice in several fields (e.g., phylogenetic inference, evolutionary biology, genomics), yet understanding how these 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 instructors 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 discuss hypothesis testing, Bayesian variable selection, and Bayesian applications in machine learning. Rather than demonstrating existing libraries or software, 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 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 scripts.The aim is that, by the end of the week, each participant will have written their own MCMC – from scratch!

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


University of Fribourg



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