• ECTS

    2 credits

  • Component

    Faculty of Science

Description

1. Bayesian inference: Motivation and simple example. 

2. The likelihood. 

3. A detour to explore priors. 

4. Markov chains Monte Carlo methods (MCMC)

5. Bayesian analyses in R with the Jags software.  

6. Contrast scientific hypotheses with model selection (WAIC).

7. Heterogeneity and multilevel models (aka mixed models. 

Read more

Objectives

1. Try and demystify Bayesian statistics, and MCMC methods 

2. Make the difference between Bayesian and Frequentist analyses 

3. Understand the Methods section of a paper that does Bayesian stuff 

4. Run Bayesian analyses with R (in Jags)

Read more

Knowledge control

100% continuous assessment

Read more

Further information

Hourly volumes* :

CM : 0 h

TD: 9 h

Practical work: 6 h

Field : 0 h

**********

SPS: 0 h

Seminars: 0 h

Outside UM: 0 h

Read more