@article {91,
	title = {Learning Markov networks networks with context-specific independences.},
	journal = { International Journal on Artificial Intelligence Tools},
	volume = {23},
	year = {2014},
	month = {12/2014},
	chapter = {1460030},
	abstract = {<p>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<br />
represent flexible independences. For instance, context-specific independences cannot be described by a single graph. To overcome this limitation, this work proposes a new alternative representation named canonical model as well as the CSPC algorithm; a novel knowledge discovery algorithm for learning canonical models by using context-specific independences as constraints. On an extensive empirical evaluation, CSPC learns more accurate structures than state-of-the-art density estimation and knowledge discovery algorithms. Moreover, for answering inference queries, our approach obtains competitive results against density estimation algorithms, significantly outperforming knowledge discovery algorithms.</p>
},
	keywords = {CSI models, Knowledge discovery, Markov networks; structure learning; context-specific independences},
	issn = {ISSN: 0218-2130},
	doi = {10.1142/S0218213014600306},
	url = {http://www.worldscientific.com/doi/abs/10.1142/S0218213014600306},
	author = {Edera, Alejandro and Schl{\"u}ter, Federico and Bromberg, Facundo}
}
