@inproceedings{4993654588dc441cb30fb367674f196c,
title = "Abstraction in Markov networks",
abstract = "In this paper a new approach is presented for taming the complexity of performing inferences on Markov networks. The approach consists in transforming the network into an abstract one, with a lower number of vertices. The abstract network is obtained through a partitioning of its set of cliques. The paper shows under what conditions exact inference may be obtained with reduced cost, and ways of partitioning the graph are discussed. An example, illustrating the method, is also described.",
keywords = "Abstraction, Approximate inference, Graphical models, Markov networks",
author = "Lorenza Saitta",
year = "2013",
doi = "10.1007/978-3-319-03524-6_13",
language = "English",
isbn = "9783319035239",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "145--156",
booktitle = "AI*IA 2013",
note = "13th International Conference of the Italian Association for Artificial Intelligence, AI*IA 2013 ; Conference date: 04-12-2013 Through 06-12-2013",
}