On the Complexity of Probabilistic Inference in Singly Connected Bayesian Networks | Zendy

Dan Wu | Zendy; Cory J. Butz | Zendy

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On the Complexity of Probabilistic Inference in Singly Connected Bayesian Networks

Author(s) -

Dan Wu,

Cory J. Butz

Publication year - 2005

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

ISBN - 3-540-28653-5

DOI - 10.1007/11548669_60

Subject(s) - inference , bayesian network , computational complexity theory , computer science , bayesian inference , probabilistic logic , bayesian probability , point (geometry) , artificial intelligence , theoretical computer science , machine learning , algorithm , mathematics , geometry

In this paper, we revisit the consensus of computational complexity on exact inference in Bayesian networks. We point out that even in singly connected Bayesian networks, which conventionally are believed to have efficient inference algorithms, the computational complexity is still NP-hard.

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