Bayesian approach to variability

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. 

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)

100% continuous assessment

Hourly volumes* :

CM : 0 h

TD: 9 h

Practical work: 6 h

Field : 0 h

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SPS: 0 h

Seminars: 0 h

Outside UM: 0 h