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Structure Learning of Contextual Markov Networks using Marginal Pseudo-likelihood
Markov networks are popular models for discrete multivariate systems where the dependence structure of the variables is specified by an undirected graph.
Context-specific independence in graphical log-linear models
Log-linear models are the popular workhorses of analyzing contingency tables.
Marginal and simultaneous predictive classification using stratified graphical models
An inductive probabilistic classification rule must generally obey the principles of Bayesian predictive inference, such that all observed and unobserved stochastic quantities are jointly modeled and the parameter uncertainty is fully acknowledged through the posterior predictive distribution.
Marginal Pseudo-Likelihood Learning of Markov Network structures
Traditionally, learning of the network structure has been done under the assumption of chordality which ensures that efficient scoring methods can be used.
Labeled Directed Acyclic Graphs: a generalization of context-specific independence in directed graphical models
We introduce a novel class of labeled directed acyclic graph (LDAG) models for finite sets of discrete variables.
Learning Chordal Markov Networks by Constraint Satisfaction
We investigate the problem of learning the structure of a Markov network from data.
Stratified Graphical Models - Context-Specific Independence in Graphical Models
Theory of graphical models has matured over more than three decades to provide the backbone for several classes of models that are used in a myriad of applications such as genetic mapping of diseases, credit risk evaluation, reliability and computer security, etc.
