Detailed information about the course
[ANNULÉ] POSTPONED TO 2024 - Introduction to Bayesian Inference in Practice
Sept 18-22, 2023
|Lang||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).
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
Full attendance and active participation.
Make sure to sign the attendance list each and every day!
Register via your MyCUSO account.
Extended deadline for registration: 05 Sept 2023
|Deadline for registration||05.09.2023|