A Comparison of Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer Architectures for Computing Marginals of Probability Distributions
This work provides a comparative analysis for researchers in probabilistic graphical models, but it is incremental as it reviews existing methods without introducing new ones.
The paper compared three architectures (Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer) for computing marginals of probability distributions, analyzing their graphical structures, message-passing schemes, computational efficiency, and storage efficiency.
In the last decade, several architectures have been proposed for exact computation of marginals using local computation. In this paper, we compare three architectures - Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer - from the perspective of graphical structure for message propagation, message-passing scheme, computational efficiency, and storage efficiency.