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A sufficiently fast algorithm for finding close to optimal junction trees

Conference Paper Accepted Paper Artificial Intelligence · Machine Learning · Uncertainty in Artificial Intelligence

Abstract

An algorithm is developed for finding a close to optimal junction tree of a given graph G. The algorithm has a worst case complexity O(c^k n^a) where a and c are constants, n is the number of vertices, and k is the size of the largest clique in a junction tree of G in which this size is minimized. The algorithm guarantees that the logarithm of the size of the state space of the heaviest clique in the junction tree produced is less than a constant factor off the optimal value. When k = O(log n), our algorithm yields a polynomial inference algorithm for Bayesian networks.

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Context

Venue
Conference on Uncertainty in Artificial Intelligence
Archive span
1985-2025
Indexed papers
3717
Paper id
853109917378654996
v2026.09.13