Abstraction in Markov networks

Lorenza Saitta

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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.

Lingua originaleInglese
Titolo della pubblicazione ospiteAI*IA 2013
Sottotitolo della pubblicazione ospiteAdvances in Artificial Intelligence - XIIIth International Conference of the Italian Association for Artificial Intelligence, Proceedings
Pagine145-156
Numero di pagine12
DOI
Stato di pubblicazionePubblicato - 2013
Pubblicato esternamente
Evento13th International Conference of the Italian Association for Artificial Intelligence, AI*IA 2013 - Turin, Italy
Durata: 4 dic 20136 dic 2013

Serie di pubblicazioni

NomeLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8249 LNAI
ISSN (stampa)0302-9743
ISSN (elettronico)1611-3349

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???event.eventtypes.event.conference???13th International Conference of the Italian Association for Artificial Intelligence, AI*IA 2013
Paese/TerritorioItaly
CittàTurin
Periodo4/12/136/12/13

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