Graphical Models A Gaussian Markov Network (MARKS)

Graphical models are defined by: • a network structure, G = (V, E), either an undirected graph (Markov networks, gene association networks, correlation networks, etc.) or a directed graph (Bayesian networks). Each node vi ∈ V corresponds to a random variable Xi; • a global probability distribution, X, which can be factorised into a set of small local probability distributions according to the edges eij ∈ E present in the graph. This combination allows a compact representation of the joint distribution of large numbers of random variables and simplifies inference on the resulting parameter space