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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… Expand
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Papers overview

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Highly Cited
2018
Highly Cited
2018
The graph convolutional networks (GCN) recently proposed by Kipf and Welling are an effective graph model for semi-supervised… Expand
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Highly Cited
2011
Highly Cited
2011
Structural reliability methods aim at computing the probability of failure of systems with respect to some prescribed performance… Expand
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Highly Cited
2011
Highly Cited
2011
Abstract Random graphs with given degrees are a natural next step in complexity beyond the Erdős–Rényi model, yet the degree… Expand
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Highly Cited
2010
Highly Cited
2010
1. Elements of Importance Sampling.- 2. Methods of Importance Sampling.- 3. Sums of Random Variables.- 4. Detection Theory.- 5… Expand
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Highly Cited
2009
Highly Cited
2009
. The Adaptive Multiple Importance Sampling algorithm is aimed at an optimal recycling of past simulations in an iterated… Expand
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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… Expand
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Highly Cited
2006
Highly Cited
2006
In this paper, we propose a novel methodology for statistical SRAM design and analysis. It relies on an efficient form of… Expand
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Highly Cited
2005
Highly Cited
2005
Monte Carlo simulation is widely used to measure the credit risk in portfolios of loans, corporate bonds, and other instruments… Expand
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Highly Cited
2001
Highly Cited
2001
  • R. Neal
  • Stat. Comput.
  • 2001
  • Corpus ID: 11112994
Simulated annealing—moving from a tractable distribution to a distribution of interest via a sequence of intermediate… Expand
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Highly Cited
1996
Highly Cited
1996
  • J. Liu
  • Stat. Comput.
  • 1996
  • Corpus ID: 13234855
Although Markov chain Monte Carlo methods have been widely used in many disciplines, exact eigen analysis for such generated… Expand
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