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NLP
2000
13 years 8 months ago
Monte-Carlo Sampling for NP-Hard Maximization Problems in the Framework of Weighted Parsing
Abstract. The purpose of this paper is (1) to provide a theoretical justification for the use of Monte-Carlo sampling for approximate resolution of NP-hard maximization problems in...
Jean-Cédric Chappelier, Martin Rajman
TCBB
2010
137views more  TCBB 2010»
13 years 2 hour ago
The Metropolized Partial Importance Sampling MCMC Mixes Slowly on Minimum Reversal Rearrangement Paths
Markov chain Monte Carlo has been the standard technique for inferring the posterior distribution of genome rearrangement scenarios under a Bayesian approach. We present here a neg...
István Miklós, Bence Melykuti, Krist...
WWW
2006
ACM
14 years 6 months ago
Random sampling from a search engine's index
We revisit a problem introduced by Bharat and Broder almost a decade ago: how to sample random pages from the corpus of documents indexed by a search engine, using only the search...
Ziv Bar-Yossef, Maxim Gurevich
ICML
2008
IEEE
14 years 6 months ago
Strategy evaluation in extensive games with importance sampling
Typically agent evaluation is done through Monte Carlo estimation. However, stochastic agent decisions and stochastic outcomes can make this approach inefficient, requiring many s...
Michael H. Bowling, Michael Johanson, Neil Burch, ...
SAC
2010
ACM
13 years 3 months ago
Importance tempering
Simulated tempering (ST) is an established Markov Chain Monte Carlo (MCMC) methodology for sampling from a multimodal density π(θ). The technique involves introducing an auxilia...
Robert B. Gramacy, Richard Samworth, Ruth King