Approximate inference algorithms for two-layer Baysian networks (1999) [3 citations — 2 self]
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by Andrew Y. Ng, Michael I. Jordan
NIPS
http://www.cs.berkeley.edu/~ang/papers/nips99-twolayerbn.ps
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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
Citations
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| 200 | Loopy belief propagation for approximate inference: An empirical study – Murphy, Weiss, et al. - 1999 |
| 81 | Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. I. The Probabilistic Model and Inference Algorithms – Shwe, Middleton, et al. - 1991 |
| 41 | Variational probabilistic inference and the QMR-DT network – Jaakkola, Jordan - 1999 |
| 36 | A tractable inference algorithm for diagnosing multiple diseases – Heckerman - 1989 |
| 30 | Convergence condition of the TAP equation for the infinite-ranged Ising spin glass model – Plefka - 1982 |
| 12 | Large deviation methods for approximate probabilistic inference, with rates of convergence – Kearns, Saul - 1998 |
| 3 | Variational cumulant expansions for intractable distributions – Barber - 1999 |
