(Enter summary)
Abstract: We present a class of approximate inference algorithms for graphical
models of the QMR-DT type. We give convergence rates for these algorithms
and for the Jaakkola and Jordan (1999) algorithm, and verify
these theoretical predictions empirically. We also present empirical results
on the difficult QMR-DT network problem, obtaining performance
of the new algorithms roughly comparable to the Jaakkola and Jordan
algorithm.
1 Introduction
The graphical models formalism provides an appealing... (Update)
Context of citations to this paper: More
.... widely used as a benchmark for approximate inference algorithms [Shwe and Cooper, 1991, D Ambrosio, 1994, Murphy et al. 1999, Ng and Jordan, 2000] The set of observable nodes in the QMR DT are called ndings 1 and the unobservable nodes are diseases. When evaluating...
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BibTeX entry: (Update)
Ng, A. Y. and Jordan, M. I. (2000). Approximate inference algorithms for two{ layer Bayesian networks. In Advances in Neural Information Processing Systems, volume 12. MIT Press. http://citeseer.ist.psu.edu/article/ng00approximate.html More
@misc{ ng00approximate,
author = "A. Ng and M. Jordan",
title = "Approximate inference algorithms for two{ layer Bayesian networks",
text = "Ng, A. Y. and Jordan, M. I. (2000). Approximate inference algorithms for
two{ layer Bayesian networks. In Advances in Neural Information Processing
Systems, volume 12. MIT Press.",
year = "2000",
url = "citeseer.ist.psu.edu/article/ng00approximate.html" }
Citations (may not include all citations):
192
An introduction to variational methods for graphical models
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