Linear Response for Approximate Inference
Abstract
Belief propagation on cyclic graphs is an efficient algorithm for comput- ing approximate marginal probability distributions over single nodes and neighboring nodes in the graph. In this paper we propose two new al- gorithms for approximating joint probabilities of arbitrary pairs of nodes and prove a number of desirable properties that these estimates fulfill. The first algorithm is a propagation algorithm which is shown to con- verge if belief propagation converges to a stable fixed point. The second algorithm is based on matrix inversion. Experiments compare a number of competing methods.
Cite
Text
Welling and Teh. "Linear Response for Approximate Inference." Neural Information Processing Systems, 2003.Markdown
[Welling and Teh. "Linear Response for Approximate Inference." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/welling2003neurips-linear/)BibTeX
@inproceedings{welling2003neurips-linear,
title = {{Linear Response for Approximate Inference}},
author = {Welling, Max and Teh, Yee W.},
booktitle = {Neural Information Processing Systems},
year = {2003},
pages = {361-368},
url = {https://mlanthology.org/neurips/2003/welling2003neurips-linear/}
}