- Knowing and understanding of the fundamental concepts of Bayes statistics (k1,k2)
- Knowing and understanding the Bayesian approach to statistical learning (k1,k2)
- Discussing and critical evaluation of choices of of prior distributions (k3,k4, k5)
- Performing a conjugate Bayesian analysis (k3)
- Understanding and implementing MCMC methods for Bayesian inference in statistical software R (k3)
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- Bayes rule and Bayes theorem
- Basic concepts of Bayesian analysis
- Conjugate prior distribution and conjugate analysis
- Conjugacy in Exponential families
- Predictive distributions and posterior predictive model checking
- Monte Carlo Approximation of the Posterior distribution
- MCMC methods: Gibbs sampling, Data Augmentation, Metropolis-Hastings Algorithm
- Bayesian analysis of regression type models (linear regression, probit model, mixed effects model)
- Bayesian analysis of finite mixture distribution
- Bayesian analysis of missing data
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