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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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a2a37e5daf1202b3972655dd63e8d793
http://arxiv.org/abs/1608.02315
http://arxiv.org/abs/1608.02315
Publikováno v:
Advances in Artificial Intelligence--IBERAMIA 2014 ISBN: 9783319120263
IBERAMIA
IBERAMIA
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:
https://explore.openaire.eu/search/publication?articleId=doi_________::4fa121494771fe674fa8fcd3aa17148b
https://doi.org/10.1007/978-3-319-12027-0_23
https://doi.org/10.1007/978-3-319-12027-0_23