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Blankets Joint Posterior score for learning Markov network structures
Markov networks are extensively used to model complex sequential, spatial, and relational interactions in a wide range of fields.
Learning Markov networks with context-specific independences
In this work we present CSPC, an independence-based algorithm for learning structures that encode context-specific independences, and encoding them in a log-linear model, instead of a graph.
Markov random fields factorization with context-specific independences
This is presented in our main contribution, the context-specific Hammersley-Clifford theorem, a generalization to CSIs of the Hammersley-Clifford theorem that applies for conditional independences.
The IBMAP approach for Markov networks structure learning
IBMAP contemplates this uncertainty in the outcome of the tests through a probabilistic maximum-a-posteriori approach.
A survey on independence-based Markov networks learning
This work reports the most relevant technical aspects in the problem of learning the \emph{Markov network structure} from data.
