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Importance sampling approximates expectations with respect to a target measure by using samples from a proposal measure.
On information and sufficiency
Kullback, S · 1951
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Methods of reducing sample size in Monte Carlo computations
Kahn, H · 1953
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On the probability of large deviations of random variables
Sanov, I. N · 1958
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Eine informationstheoretische ungleichung und ihre anwendung auf den beweis der ergodizität von markoffschen ketten
Csiszár, I · 1963
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A general class of coefficients of divergence of one distribution from another
Ali, S. M · 1966
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Information-type measures of difference of probability distributions and indirect observations
Csiszár, I · 1967
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Importance sampling in the Monte Carlo study of sequential tests
Siegmund, D · 1976
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Convex Statistical Distances
Liese, F · 1987
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An Introduction to Sequential Monte Carlo Methods
Doucet, A · 2001
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On choosing and bounding probability metrics
Gibbs, A. L · 2002
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Information theory and statistics: A tutorial
Csiszár, I · 2004
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Feynman-Kac Formulae
Del Moral, P · 2004
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Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems
Bengtsson, T · 2008
Cited alongside, same era.
Monte Carlo Strategies in Scientific Computing
Liu, J. S · 2008
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Large deviations for weighted empirical measures arising in importance sampling
Hult, H · 2012
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Conditions for successful data assimilation
Chorin, A. J · 2013
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Importance sampling: computational complexity and intrinsic dimension
Agapiou, S · 2015
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The sample size required in importance sampling
Chatterjee, S · 2015
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Sason, I · 2015
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