On the Robustness of Most Probable Explanations
Artificial Intelligence
2012-07-02 v1
Abstract
In Bayesian networks, a Most Probable Explanation (MPE) is a complete variable instantiation with a highest probability given the current evidence. In this paper, we discuss the problem of finding robustness conditions of the MPE under single parameter changes. Specifically, we ask the question: How much change in a single network parameter can we afford to apply while keeping the MPE unchanged? We will describe a procedure, which is the first of its kind, that computes this answer for each parameter in the Bayesian network variable in time O(n exp(w)), where n is the number of network variables and w is its treewidth.
Cite
@article{arxiv.1206.6819,
title = {On the Robustness of Most Probable Explanations},
author = {Hei Chan and Adnan Darwiche},
journal= {arXiv preprint arXiv:1206.6819},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI2006)