An e-cient factorization for the noisy MAX ⁄
Francisco J. D, Severino F. Galán · 2007
D¶‡ez’s algorithm for the noisy MAX is very e‐cient for polytrees, but when the network has loops it has to be combined with local conditioning, a suboptimal propagation algorithm. Other algorithms, based on several factorizations of the conditional probability of the noisy MAX, are not as e‐cient for polytrees, but can be combined with general propagation algorithms, such as clustering or variable elimination, which are more e‐cient for networks with loops. In this paper we propose a new factorization of the noisy MAX that amounts to D¶‡ez’s algorithm in the case of polytrees and at the same time is more e‐cient than previous factorizations when combined with either variable elimination or clustering.