See this document in CiteSeerX!

Regularized Greedy Importance Sampling  (Make Corrections)  
Finnegan Southey Dale Schuurmans Ali Ghodsi School of Computer Science...



  Home/Search   Context   Related

 
View or download:
books.nips.cc/papers/files...AA34.ps.gz
Cached:  PS.gz  PS  PDF   Image  Update  Help

From:  books.nips.cc/nips15 (more)
(Enter author homepages)

Rate this article: (best)
  Comment on this article  
(Enter summary)

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)

Similar documents based on text:   More   All
1.4:   Regularized Greedy Importance Sampling - Finnegan Southey Dale   (Correct)
0.4:   An Adaptive Regularization Criterion for Supervised Learning - Schuurmans, Southey (2000)   (Correct)
0.3:   The Exponentiated Subgradient Algorithm for Heuristic.. - Schuurmans, Southey.. (2001)   (Correct)

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]

Documents on the same site (http://books.nips.cc/nips15.html):   More
Learning Attractor Landscapes for Learning Motor Primitives - Ijspeert, Nakanishi, Schaal (2003)   (Correct)
A Statistical Mechanics Approach to Approximate Analytical.. - Malzahn, Opper (2003)   (Correct)
Going Metric: Denoising Pairwise Data - Roth, Laub, Buhmann, Müller (2002)   (Correct)

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC