A New Algorithm for Finding MAP Assignments to Belief Networks
Abstract
We present a new algorithm for finding maximum a-posterior) (MAP) assignments of values to belief networks. The belief network is compiled into a network consisting only of nodes with boolean (i.e. only 0 or 1) conditional probabilities. The MAP assignment is then found using a best-first search on the resulting network. We argue that, as one would anticipate, the algorithm is exponential for the general case, but only linear in the size of the network for poly trees.
Cite
Text
Shimony and Charniak. "A New Algorithm for Finding MAP Assignments to Belief Networks." Conference on Uncertainty in Artificial Intelligence, 1990.Markdown
[Shimony and Charniak. "A New Algorithm for Finding MAP Assignments to Belief Networks." Conference on Uncertainty in Artificial Intelligence, 1990.](https://mlanthology.org/uai/1990/shimony1990uai-new/)BibTeX
@inproceedings{shimony1990uai-new,
title = {{A New Algorithm for Finding MAP Assignments to Belief Networks}},
author = {Shimony, Solomon Eyal and Charniak, Eugene},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1990},
pages = {185-196},
url = {https://mlanthology.org/uai/1990/shimony1990uai-new/}
}