We realise that, due to the COVID-19 virus outbreak, making travel arrangements during this time is difficult. Therefore, registration for this courses does not require payment at this moment. We will only receive payments once the outbreak has calmed down and travel restrictions have been lifted.
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 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. 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 framework.
- Morning: Introduction to probabilistic models and Bayes theorem. We’ll learn:
- How to calculate the likelihood of any dataset under a simple model
- The Bayes principles (what is a prior? What is a posterior probability?)
- Afternoon: (practical) Write an R (or Python) script to compute the likelihood of data under Normal and Gamma models. 3D plots of the likelihood surface.
- Morning: basic structure of Markov Chain Monte Carlo, the most popular algorithm in Bayesian analysis.
- Afternoon: (practical) MCMC, how it works, how to implement it. Based on the likelihood functions written on Monday, implement an MCMC to fit normal and gamma distributions.
- Morning: What is the difference between modeling a pattern and modeling a process? When should we prefer one or the other? (practical) Analysis of global temperature data (provided) to estimate the existence of any climatic trends.
- Afternoon: Hypothesis testing using marginal likelihoods. (practical) How to interpret and summarize the results of an MCMC, how to assess if it worked.
- Morning: Bayesian tricks to avoid model testing: Hierarchical modeling, shrinkage, and Bayesian variable selection.
- Afternoon: (practical) Continue working on the MCMC script and with own data.
- Morning: Alternative algorithms in Bayesian analyses: Gibbs sampling and Approximate Bayesian Computation (ABC). Basic principles of machine learning.
- Afternoon: (practical) Finalize the MCMC script and (if applicable) plan the future development of the methods implemented for analysis of own data.
You can check the list of Ambassador Institutions HERE. If you want your institution to become a Transmitting Science Ambassador please contact us at email@example.com.
Discounts (see Funding below) are not cumulative and apply only on the fee.
We offer the possibility of paying in two instalments (contact the course coordinators).
- Monday to Friday:
- 9:30 to 13:00 Lessons.
- 13:00 to 14:00 Lunch (included).
- 14:00 to 17:00 Lessons.
Accommodation Package Schedule
- 19:00 Meeting point in Plaza Catalunya (Barcelona) to take the bus to Capellades. If you are planning to arrive later, you can find more information in How to get there.
- 20:00 Registration in the Hotel.
- 20:30 Dinner.
- Monday to Thursday:
- 8:00 to 9:00 Breakfast.
- 20:30 Dinner.
- 8:00 to 9:00 Breakfast.
- 17:30 Meeting point in the Hotel to take the bus to Barcelona city.
- 19:00 Arrival at Plaza Catalunya (Barcelona).
The schedule is approximate; it is possible that the content of one day may run into the next and a working day may be longer than advertised.
Former participants will have a 5 % discount on the Course Fee.
Furthermore, a 20 % discount on the Course Fee is offered for members of some organizations (Organizations with discount). If you want to apply to this discount please indicate it in the Registration form (proof will be asked later).
Unemployed scientists living in Spain, as well as PhD students based in Spain without any grant or scholarship to develop their PhD, could benefit from a 40 % discount on the Course Fee. If you want to ask for this discount, please contact the course coordinator. That would apply for a maximum of 2 places and they will be covered by strict inscription order.
Discounts are not cumulative and apply only on the fee, not to Accommodation Package or other options.