Search Results for author:
Found 4 papers, 0 papers with code
The Grow-Shrink strategy for learning Markov network structures constrained by context-specific independences
The structure qualitatively describes independences in the distribution, which can be exploited to factorize the distribution into a set of compact functions.
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.
