Recursive Algorithms for Approximating Probabilities in Graphical Models
Abstract
We develop a recursive node-elimination formalism for efficiently approximating large probabilistic networks. No constraints are set on the network topologies. Yet the formalism can be straightfor(cid:173) wardly integrated with exact methods whenever they are/become applicable. The approximations we use are controlled: they main(cid:173) tain consistently upper and lower bounds on the desired quantities at all times. We show that Boltzmann machines, sigmoid belief networks, or any combination (i.e., chain graphs) can be handled within the same framework. The accuracy of the methods is veri(cid:173) fied experimentally.
Cite
Text
Jaakkola and Jordan. "Recursive Algorithms for Approximating Probabilities in Graphical Models." Neural Information Processing Systems, 1996.Markdown
[Jaakkola and Jordan. "Recursive Algorithms for Approximating Probabilities in Graphical Models." Neural Information Processing Systems, 1996.](https://mlanthology.org/neurips/1996/jaakkola1996neurips-recursive/)BibTeX
@inproceedings{jaakkola1996neurips-recursive,
title = {{Recursive Algorithms for Approximating Probabilities in Graphical Models}},
author = {Jaakkola, Tommi and Jordan, Michael I.},
booktitle = {Neural Information Processing Systems},
year = {1996},
pages = {487-493},
url = {https://mlanthology.org/neurips/1996/jaakkola1996neurips-recursive/}
}