65 citations · 101 across the 11 of their papers we have counts for
5 papers · 1 filter
Blankets Joint Posterior score for learning Markov network structures
Federico Schlüter, Yanela Strappa, Diego H. Milone +1
Markov networks are extensively used to model complex sequential, spatial, and relational interactions in a wide range of fields. By learning the structure of independences of a do…
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…
Efficient Independence-Based MAP Approach for Robust Markov Networks Structure Discovery
Facundo Bromberg, Federico Schlüter
This work introduces the IB-score, a family of independence-based score functions for robust learning of Markov networks independence structures. Markov networks are a widely used…