Probabilistic Graphical Models and Their Inferences (Tutorial)

Probabilistic graphical models are useful for modelling stochastic phenomena for doing inferences and reasoning under uncertainty. Especially, chain graph models and Bayesian networks can be used as probabilistic expert systems where inferences can be done with junction tree algorithm, etc. And they can be extended to capture multi-stage decision contexts. Fundamentally these models capture (in) dependence structure of the context, but model learning is hard in practice. There are methods to do this, from simple independence test-based ones to more advanced score-based methods. When these models are used as classifiers, model learning can be done discriminatively, thus resulting higher classification accuracies in them.