Learning Markov networks networks with context-specific independences.
|Title||Learning Markov networks networks with context-specific independences.|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Edera A, Schlüter F, Bromberg F|
|Journal||International Journal on Artificial Intelligence Tools|
|Keywords||CSI models, Knowledge discovery, Markov networks; structure learning; context-specific independences|
This work focuses on learning the structure of Markov networks from data. Markov networks are parametric models for compactly representing complex probability distributions. These models are composed by: a structure and numerical weights, where the structure describes independences that hold in the distribution. Depending on which is the goal of structure learning, learning algorithms can be divided into: density estimation algorithms, where structure is learned for answering inference queries; and knowledge discovery algorithms, where structure is learned for describing independences qualitatively. The latter algorithms present an important limitation for describing independences because they use a single graph; a coarse grain structure representation which cannot