8 citations · 25 across the 7 of their papers we have counts for
7 papers
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…
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…
A survey on independence-based Markov networks learning
Federico Schlüter
This work reports the most relevant technical aspects in the problem of learning the \emph{Markov network structure} from data. Such problem has become increasingly important in ma…
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…