Bayesian

Markov Network For a set of variables $X=\{x1,...,xn\}$ a Markov network is defined as a product of potentials on subsets of the variables $X_c \subs

InferenceUsed to answer queries about $P_M,\\ e.g.,\\ P_M(X|Y)$LearningUsed to estimate a plausible model $M$ from data $D$1\. LikelihoodEvidence $e$

Message Passing is a process of converting directed graphical model and moralizing the graph.ExampleLet's assume we have a direct graph as below.The j

Bayesian Networks is a Directed Graphical ModelMarkov Network is an Undirected Graphical Model

$$p(x;\\theta)=exp(\\theta^T T(x) - A(\\theta))h(x)$$$A(\\theta)$ is called log partition function.$T(x)$ is called sufficient statistics.$A(\\theta)$

Graphical model structure is givenEvery variable appears in the training examples (no unobserved)What does the likelihood objective accomplish?Is like