Bayesian approach to variability

1. Bayesian inference: Motivation and simple example. 

2. The likelihood. 

3. A detour to explore priors. 

4. Markov chain Monte Carlo methods (MCMC)

5. Bayesian analyses in R with the Jags software.  

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

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

1. Try and demystify Bayesian statistics and MCMC methods 

2. Distinguish 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)

Continuous assessment: 100%

Hourly volumes:

CM: 0 hours

Tutorial: 9 a.m.

Practical work: 6 hours

Field: 0 hours

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SPS: 0 hours

Seminars: 0 hours

Outside UM: 0 hours