An alternative proof that exact inference problem in bayesian belief networks is NP-hard

Authors:
Mustafa Tacettin;Tonguç Ünlüyurt
Affiliations:
Faculty of Engineering and Natural Sciences, Sabancı University, Orhanli, Istanbul, Turkey;Faculty of Engineering and Natural Sciences, Sabancı University, Orhanli, Istanbul, Turkey
Venue:
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
Year:
2005

Citing 8
Cited 0

Fusion, propagation, and structuring in belief networks

Artificial Intelligence
Probabilistic inference and influence diagrams

Operations Research
The computational complexity of probabilistic inference using Bayesian belief networks (research note)

Artificial Intelligence
Approximating probabilistic inference in Bayesian belief networks is NP-hard

Artificial Intelligence
On the hardness of approximate reasoning

Artificial Intelligence
An optimal approximation algorithm for Bayesian inference

Artificial Intelligence
Approximation algorithms

Approximation algorithms
Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness

Quantified Score

Hi-index	0.00

Visualization

Abstract

Exact inference problem in belief networks has been well studied in the literature and has various application areas. It is known that this problem and its approximation version are NP-hard. In this study, an alternative polynomial time transformation is provided from the well-known vertex cover problem. This new transformation may lead to new insights and polynomially solvable classes of the exact inference problem in Bayesian belief networks.