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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 domain, more accurate joint probability distributions can be obtained fo
Externí odkaz:
http://arxiv.org/abs/1608.02315
Markov networks are models for compactly representing complex probability distributions. They are composed by a structure and a set of numerical weights. The structure qualitatively describes independences in the distribution, which can be exploited
Externí odkaz:
http://arxiv.org/abs/1407.8088
Publikováno v:
In International Journal of Approximate Reasoning January 2018 92:295-320
Publikováno v:
Advances in Artificial Intelligence -- IBERAMIA 2014; 2014, p805-807, 3p
Autor:
Ana L.C. Bazzan, Karim Pichara
This book constitutes the refereed proceedings of the 14th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2014, held in Santiago de Chile, Chile, in November 2014. The 64 papers presented were carefully reviewed and selected from 136