A Revolution: Belief Propagation in Graphs with Cycles
Abstract
Until recently, artificial intelligence researchers have frowned upon the application of probability propagation in Bayesian belief net(cid:173) works that have cycles. The probability propagation algorithm is only exact in networks that are cycle-free. However, it has recently been discovered that the two best error-correcting decoding algo(cid:173) rithms are actually performing probability propagation in belief networks with cycles.
Cite
Text
Frey and MacKay. "A Revolution: Belief Propagation in Graphs with Cycles." Neural Information Processing Systems, 1997.Markdown
[Frey and MacKay. "A Revolution: Belief Propagation in Graphs with Cycles." Neural Information Processing Systems, 1997.](https://mlanthology.org/neurips/1997/frey1997neurips-revolution/)BibTeX
@inproceedings{frey1997neurips-revolution,
title = {{A Revolution: Belief Propagation in Graphs with Cycles}},
author = {Frey, Brendan J. and MacKay, David J. C.},
booktitle = {Neural Information Processing Systems},
year = {1997},
pages = {479-485},
url = {https://mlanthology.org/neurips/1997/frey1997neurips-revolution/}
}