approximate inference in layered networks XuanLong Nguyen Berkeley, CA 94720...  Home/Search
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Abstract: We present an analysis of concentration-of-expectation phenomena in layered Bayesian networks that use generalized linear models as the local conditional probabilities. This framework encompasses a wide variety of probability distributions, including both discrete and continuous random variables. We utilize ideas from large deviation analysis and the delta method to devise and evaluate a class of approximate inference algorithms for layered Bayesian networks that have superior asymptotic... (Update)
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BibTeX entry: (Update)
@misc{ in-concentration,
author = "Approximate Inference In",
title = "On the Concentration of Expectation and",
url = "citeseer.ist.psu.edu/756526.html" }
Citations (may not include all citations):520 Generalized Linear Models (context) - McCullagh, Nelder - 1983
245 An introduction to variational methods for graphical models - Jordan, Ghahramani et al. - 1998
82 Generalized belief propagation - Yedidia, Freeman et al. - 2001
36 Fundamentals of Statistical Exponential Families with Applic.. (context) - Brown - 1986
25 Expectation propagation for approximate Bayesian inference (context) - Minka - 2001
23 A tractable inference algorithm for diagnosing multiple dise.. (context) - Heckerman - 1989
10 Convergence condition of the TAP equation for the infinite-r.. (context) - Plefka - 1982
8 Large deviation methods for approximate probabilistic infere.. - Kearns, Saul - 1998
4 Variational cumulant expansions for intractable distribution.. - Barber, Laar - 1999
2 Inference in multi-layer networks via large deviation bounds - Kearns, Saul - 1999
2 Approximate inference algorithms for two-layer Baysian netwo.. - Ng, Jordan - 2000
1 Gaussian fields for approximate inference in layered sigmoid.. (context) - Barber, Sollich - 1999
1 the concentration of expectation and approximate inference i.. - Nguyen, Jordan - 2003
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