@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 = {<a href="http://mlg.eng.cam.ac.uk/frederik/libdai-eaton.tar.gz">Code</a>
            (in C++ based on <a href="http://www.kyb.mpg.de/bs/people/jorism/libDAI">libDAI</a>).},
  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.}
}