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Abstract: Greedy importance sampling is an unbiased estimation technique that reduces the variance of standard importance sampling by explicitly searching for modes in the estimation objective. Previous work has demonstrated the feasibility of implementing this method and proved that the technique is unbiased in both discrete and continuous domains. In this paper we present a reformulation of greedy importance sampling that eliminates the free parameters from the original estimator, and introduces ... (Update)
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BibTeX entry: (Update)
@misc{ dale-regularized,
author = "Finnegan Southey Dale",
title = "Regularized Greedy Importance Sampling",
url = "citeseer.ist.psu.edu/703640.html" }
Citations (may not include all citations):461 Markov Chain Monte Carlo in Practice (context) - Gilks, Richardson et al. - 1996
376 A learning algorithm for Boltzmann machines (context) - Ackley, Hinton et al. - 1985
245 An introduction to variational methods for graphical models - Jordan, Ghahramani et al. - 1998
199 Probabilistic inference using Markov chain Monte Carlo metho.. - Neal - 1993
182 Exact sampling with coupled Markov chains and applications t.. - Propp, Wilson - 1996
126 Simulation and the Monte Carlo Method (context) - Rubinstein - 1981
108 Approximating probabilistic inference in Bayesian belief net.. (context) - Dagum, Luby - 1993
62 Tools for Statistical Inference: Methods for Exploration of .. (context) - Tanner - 1993
20 An optimal approximation algorithm for Bayesian inference - Dagum, Luby - 1997
6 New Monte Carlo methods for improved efficiency of computer .. (context) - Swendsen, Wang et al. - 1992
5 Intro to Monte Carlo methods (context) - MacKay - 1998
4 Greedy importance sampling - Schuurmans - 1999
4 Baysian inference in econometric models using Monte Carlo in.. (context) - Geweke - 1989
2 Sampling configurations of an Ising system (context) - Wilson - 1999
2 Monte Carlo inference via greedy importance sampling - Schuurmans, Southey - 2000
[Article contains additional citations not shown here]
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