Importance sampling

Known as: IS 
In statistics, importance sampling is a general technique for estimating properties of a particular distribution, while only having samples generated… (More)
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Papers overview

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Review
2015
Review
2015
The basic idea of importance sampling is to use independent samples from a proposal measure in order to approximate expectations… (More)
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Highly Cited
2014
Highly Cited
2014
Uniform sampling of training data has been commonly used in traditional stochastic optimization algorithms such as Proximal… (More)
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Highly Cited
2012
Highly Cited
2012
The Adaptive Multiple Importance Sampling (AMIS) algorithm is aimed at an optimal recycling of past simulations in an iterated… (More)
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Highly Cited
2008
Highly Cited
2008
In this paper, we propose an adaptive algorithm that iteratively updates both the weights and component parameters of a mixture… (More)
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Highly Cited
2005
Highly Cited
2005
The paper describes a simple, generic and yet highly accurate Efficient Importance Sampling (EIS) Monte Carlo (MC) procedure for… (More)
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Highly Cited
2005
Highly Cited
2005
simulation problem because default probabilities are low for highly rated obligors and because risk management is particularly… (More)
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Highly Cited
2005
Highly Cited
2005
We present a new technique for importance sampling products of complex functions using wavelets. First, we generalize previous… (More)
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Highly Cited
2001
Highly Cited
2001
Abstract. Simulated annealing — moving from a tractable distribution to a distribution of interest via a sequence of intermediate… (More)
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Highly Cited
1997
Highly Cited
1997
Computing (ratios of) normalizing constants of probability models is a fundamental computational problem for many statistical and… (More)
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Highly Cited
1996
Highly Cited
1996
Although Markov chain Monte Carlo methods have been widely used in many disciplines, exact eigen analysis for such generated… (More)
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