the probabilistic graphical models section — bayesian and markov networks, exact and approximate inference, sequence models, and learning from data. built from unsw's comp9418.
Graphical-Models
approximate inference in graphical models — forward and rejection sampling, likelihood weighting, gibbs sampling and mcmc, and loopy belief propagation, with the trade-offs that decide which to reach for.
bayesian networks from first principles — the chain rule, conditional probability tables, d-separation and its valve intuition, markov blankets, i-maps, naive bayes classifiers and gaussian networks.
exact inference in graphical models — factors, variable elimination, elimination orders and treewidth, the jointree (junction tree) algorithm with message passing, and mpe/map queries.
sequence models as graphical models — markov chains and stationary distributions, hidden markov models, the forward and viterbi algorithms on the state trellis, particle filtering, and dynamic bayesian networks.
undirected graphical models — gibbs distributions and the partition function, hammersley-clifford, factor graphs, energy minimisation and map-mrf, separation semantics, moralisation, and conditional random fields.