@string{jmlr = {Journal of Machine Learning Research}}
@string{aistats12 = {Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics}}
@inproceedings{eaton2009choosing,
author = {Frederik Eaton and Zoubin Ghahramani},
title = {Choosing a Variable to Clamp: {A}pproximate
Inference Using Conditioned Belief Propagation},
booktitle = aistats12,
pages = {145--152},
year = 2009,
editor = {D. van Dyk and M. Welling},
volume = 5,
address = {Clearwater Beach, FL, USA},
month = {April},
publisher = jmlr,
annote = {Code
(in C++ based on libDAI).},
abstract = {In this paper we propose an algorithm for
approximate inference on graphical models based on
belief propagation (BP). Our algorithm is an
approximate version of Cutset Conditioning, in which
a subset of variables is instantiated to make the
rest of the graph singly connected. We relax the
constraint of single-connectedness, and select
variables one at a time for conditioning, running
belief propagation after each selection. We consider
the problem of determining the best variable to
clamp at each level of recursion, and propose a fast
heuristic which applies back-propagation to the BP
updates. We demonstrate that the heuristic performs
better than selecting variables at random, and give
experimental results which show that it performs
competitively with existing approximate inference
algorithms.}
}