7 citations · 11 across the 4 of their papers we have counts for
4 papers
The Grow-Shrink strategy for learning Markov network structures constrained by context-specific independences
Alejandro Edera, Yanela Strappa, Facundo Bromberg
Markov networks are models for compactly representing complex probability distributions. They are composed by a structure and a set of numerical weights. The structure qualitativel…
Learning Markov networks with context-specific independences
Alejandro Edera, Federico Schlüter, Facundo Bromberg
Learning the Markov network structure from data is a problem that has received considerable attention in machine learning, and in many other application fields. This work focuses o…
Markov random fields factorization with context-specific independences
Alejandro Edera, Facundo Bromberg, Federico Schlüter
Markov random fields provide a compact representation of joint probability distributions by representing its independence properties in an undirected graph. The well-known Hammersl…
The IBMAP approach for Markov networks structure learning
Federico Schlüter, Facundo Bromberg, Alejandro Edera
In this work we consider the problem of learning the structure of Markov networks from data. We present an approach for tackling this problem called IBMAP, together with an efficie…