Approximate Inference a Lgorithms for Two-Layer Bayesian Networks
Abstract
We present a class of approximate inference algorithms for graphical models of the QMR-DT type. We give convergence rates for these al(cid:173) gorithms and for the Jaakkola and Jordan (1999) algorithm, and verify these theoretical predictions empirically. We also present empirical re(cid:173) sults on the difficult QMR-DT network problem, obtaining performance of the new algorithms roughly comparable to the Jaakkola and Jordan algorithm.
Cite
Text
Ng and Jordan. "Approximate Inference a Lgorithms for Two-Layer Bayesian Networks." Neural Information Processing Systems, 1999.Markdown
[Ng and Jordan. "Approximate Inference a Lgorithms for Two-Layer Bayesian Networks." Neural Information Processing Systems, 1999.](https://mlanthology.org/neurips/1999/ng1999neurips-approximate/)BibTeX
@inproceedings{ng1999neurips-approximate,
title = {{Approximate Inference a Lgorithms for Two-Layer Bayesian Networks}},
author = {Ng, Andrew Y. and Jordan, Michael I.},
booktitle = {Neural Information Processing Systems},
year = {1999},
pages = {533-539},
url = {https://mlanthology.org/neurips/1999/ng1999neurips-approximate/}
}