Probabilistic Graphical Models
- Bayesian network (BN)
A directed graph whose nodes represent variables, and edges represent influences. Together with conditional probability distributions, a Bayesian network represents the joint probability distribution of its variables.
- Conditional probability distribution
Assignment of probabilities to all instances of a set of variables when the value of one or more variables is known.
- Conditional random field (CRF)
A partially directed graph that represents a conditional distribution.
- Factor graph
A type of parameterization of PGMs in the form of bipartite graphs of factor nodes and variable nodes, where a factor node indicates that the variable nodes is connected to form a clique in a PGM.
A set of nodes and edges, where edges connect pairs of nodes.
Process of answering queries using the distribution as the model of the world.
- Joint probability distribution
Assignment of probabilities to...
KeywordsBayesian Network Markov Random Field Joint Probability Distribution Conditional Random Field Variable Node
The author wishes to thank his teaching and research assistants for the PGM course (CSE 674 at the University at Buffalo). In particular, Dmitry Kovalenko, Yingbo Zhao, Chang Su, and Yu Liu for many discussions.
- Srihari S. Lecture slides and videos on machine learning and PGMs at http://www.cedar.buffalo.edu/~srihari/CSE574