Abstraction in Markov networks

Lorenza Saitta

doi:10.1007/978-3-319-03524-6_13

Abstraction in Markov networks

Lorenza Saitta

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	AI*IA 2013
Subtitle of host publication	Advances in Artificial Intelligence - XIIIth International Conference of the Italian Association for Artificial Intelligence, Proceedings
Pages	145-156
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-319-03524-6_13
Publication status	Published - 2013
Externally published	Yes
Event	13th International Conference of the Italian Association for Artificial Intelligence, AI*IA 2013 - Turin, Italy Duration: 4 Dec 2013 → 6 Dec 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8249 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th International Conference of the Italian Association for Artificial Intelligence, AI*IA 2013
Country/Territory	Italy
City	Turin
Period	4/12/13 → 6/12/13

Keywords

Abstraction
Approximate inference
Graphical models
Markov networks

Access to Document

10.1007/978-3-319-03524-6_13

Cite this

Saitta, L. (2013). Abstraction in Markov networks. In AI*IA 2013: Advances in Artificial Intelligence - XIIIth International Conference of the Italian Association for Artificial Intelligence, Proceedings (pp. 145-156). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8249 LNAI). https://doi.org/10.1007/978-3-319-03524-6_13

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Saitta, L 2013, Abstraction in Markov networks. in AI*IA 2013: Advances in Artificial Intelligence - XIIIth International Conference of the Italian Association for Artificial Intelligence, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8249 LNAI, pp. 145-156, 13th International Conference of the Italian Association for Artificial Intelligence, AI*IA 2013, Turin, Italy, 4/12/13. https://doi.org/10.1007/978-3-319-03524-6_13

Abstraction in Markov networks. / Saitta, Lorenza.
AI*IA 2013: Advances in Artificial Intelligence - XIIIth International Conference of the Italian Association for Artificial Intelligence, Proceedings. 2013. p. 145-156 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8249 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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.

KW - Abstraction

KW - Approximate inference

KW - Graphical models

KW - Markov networks

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - AI*IA 2013

T2 - 13th International Conference of the Italian Association for Artificial Intelligence, AI*IA 2013

Y2 - 4 December 2013 through 6 December 2013

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Saitta L. Abstraction in Markov networks. In AI*IA 2013: Advances in Artificial Intelligence - XIIIth International Conference of the Italian Association for Artificial Intelligence, Proceedings. 2013. p. 145-156. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-03524-6_13

Abstraction in Markov networks

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Keywords

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