Optimal Decomposition of Belief Networks
Wilson X. Wen
TL;DR
This paper explores optimal methods for decomposing belief networks, introduces a new MTNS method, and discusses the NP-hardness of the problem, proposing a simulated annealing algorithm for decomposition.
Contribution
It presents a novel decomposition method called MTNS and analyzes the computational complexity of belief network decomposition.
Findings
The problem is NP-hard.
A new decomposition method MTNS is proposed.
An algorithm based on simulated annealing is discussed.
Abstract
In this paper, optimum decomposition of belief networks is discussed. Some methods of decomposition are examined and a new method - the method of Minimum Total Number of States (MTNS) - is proposed. The problem of optimum belief network decomposition under our framework, as under all the other frameworks, is shown to be NP-hard. According to the computational complexity analysis, an algorithm of belief network decomposition is proposed in (Wee, 1990a) based on simulated annealing.
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Taxonomy
TopicsBayesian Modeling and Causal Inference · Machine Learning and Algorithms · Neural Networks and Applications