Overview

Data analysis is fundamental for arriving at scientific conclusions and testing different model hypotheses. Key to this is understanding uncertainty in our results, and Bayesian statistics offers a framework to quantify and assess the variability in our inference from data.

This 2-day course will introduce participants to the core concepts of Bayesian statistics through lectures and practical exercises. The exercises will be implemented in the widely used R programming language and the Rstan library. They will enable participants to use standard Bayesian statistical tools and interpret their results.

Audience

This course is intended for life scientists familiar with statistical inference and who would like to add the Bayesian perspective to enrich their research.

Learning outcomes

At the end of the course, participants should be able to:
* Recognise the core components of a Bayesian model
* List the main concepts of methods for Bayesian inference
* Implement a simple Bayesian model in R
* Interpret the results of a Bayesian model

Prerequisites

Knowledge / competencies

Being at ease with R is absolutely required for this course (at least equivalent to the First steps with R SIB course). Basic knowledge of statistical inference, T-test, P-values and confidence intervals (for instance, equivalent to the Introduction to statistics SIB course) is also required.

Both pre-requisites are also taught by the ETH Zurich course introduction to statistics and R

Technical

You are required to bring your own laptop and make sure that the following software is installed PRIOR to the course:

* A recent version of R and RStudio (the free version is more than enough).

Additionally, make sure to have the following R libraries installed:
* The Rstan package (warning, there are 2 steps to the installation: Configuring C++ toolchains, and then installation of Rstan)
* Rmarkdown
* Shiny
* tidyverse
* BRMS

Schedule

Day 1

9:00 – 17:00: Jack Kuipers (ETH Zurich and SIB) and Wandrille Duchemin (University of Basel and SIB)
* Monte Carlo methods
* Bayesian first steps
* Bayesian t-tests (STAN + BRMS)

Day 2

9:00 – 17:00: Jack Kuipers (ETH Zurich and SIB) and Wandrille Duchemin (University of Basel and SIB)
* Priors
* Bayesian linear regression
* Bayesian logistic regression

Application

The registration fees for academics are 200 CHF and 1000 CHF for for-profit companies.

While participants are registered on a first come, first served basis, exceptions may be made to ensure diversity and equity, which may increase the time before your registration is confirmed.

You will be informed by email of your registration confirmation. Upon reception of the confirmation email, participants will be asked to confirm attendance by paying the fees within 5 days.

Applications close on 14/04/2025 or as soon as the course is full. Deadline for free-of-charge cancellation is set to 21/04/2025. Cancellation after this date will not be reimbursed. Please note that participation in SIB courses is subject to our general conditions.

Venue and Time

This course will be in Basel, at the University of Basel.

The course will start at 9:00 and end around 17:00.

More information will be provided to the registered participants one week before the course starts.

Additional information

Coordination: Monique Zahn, SIB Training group.

We will recommend 0.5 ECTS credits for this course (given that a successful evaluation is achieved at the end of the course).

You are welcome to register to the SIB courses mailing list to be informed of all future courses and workshops, as well as all important deadlines using the form here.

Please note that participation in SIB courses is subject to our general conditions.

SIB abides by the ELIXIR Code of Conduct. Participants of SIB courses are also required to abide by the same code.

For more information, please contact [email protected].