A Bayesian Approach to Variability

1. Bayesian inference: Motivation and a simple example. 

2. The likelihood. 

3. A detour to explore priors. 

4. Markov chain Monte Carlo (MCMC) methods

5. Bayesian analyses in R using the Jags software.  

6. Compare scientific hypotheses using model selection (WAIC).

7. Heterogeneity and multilevel models (also known as mixed models). 

1. Try to demystify Bayesian statistics and MCMC methods 

2. Distinguish between Bayesian and frequentist analyses 

3. Understand the "Methods" section of a paper that uses Bayesian methods 

4. Run Bayesian analyses with R (using Jags)

Comprehensive ongoing assessment: 100%

Hourly volumes*:

CM: 12:00 a.m.

TD: 9 a.m.

Practical training: 6 hours

Land: 0 h

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SPS: 12:00 a.m.

Seminars: 0 hours

Excluding UM: 0 h