• 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. 

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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)

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Knowledge control

Continuous assessment : 100%.

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Additional information

Hourly volumes* :

CM : 0 h

TD : 9 h

TP : 6 h

Field : 0 h

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SPS : 0 h

Seminars : 0 h

Outside UM : 0 h

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