- Probabilistic formulation of machine learning problems. Bayesian interpretation of probabilities.
- Model selection problems. Bayesian model selection.
- Generalized linear models. Relevance vector machines. Automatic relevance determination.
- Local variational mathods. Bayesian regularization of logistic regression.
- General form of EM-algorithm.
- Probabilistic PCA.
- Variational Bayesian inference.
- Gaussian mixture model with automatic determination of the number of components.
- Monte Carlo Markov chain. Phase transitions in complex probabilistic models.
- Latent Dirichlet allocation as discrete mixture model.
- Gaussian processes for regression and classification.
- Dirichlet processes. Non-parametric Bayes.
- Expectation propagation and its application to recommender systems.
- Message-passing interface as general framework for disributed Bayesian inference.