Results 11 to 20 of about 2,693,397 (282)
Importance driven environment map sampling [PDF]
IEEE Transactions on Visualization and Computer Graphics, 2014 In this paper we present an automatic and efficient method for supporting Image Based Lighting (IBL) for bidirectional methods which improves both the sampling of the environment, and the detection and sampling of important regions of the scene, such as ...
Bashford-Rogers, Thomas +2 more
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Implicitly adaptive importance sampling [PDF]
Statistics and Computing, 2021 AbstractAdaptive importance sampling is a class of techniques for finding good proposal distributions for importance sampling. Often the proposal distributions are standard probability distributions whose parameters are adapted based on the mismatch between the current proposal and a target distribution.
Topi Paananen +3 more
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Generalized Multiple Importance Sampling [PDF]
Statistical Science, 2019 Importance Sampling methods are broadly used to approximate posterior distributions or some of their moments. In its standard approach, samples are drawn from a single proposal distribution and weighted properly. However, since the performance depends on the mismatch between the targeted and the proposal distributions, several proposal densities are ...
Elvira, Víctor +3 more
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Layered adaptive importance sampling [PDF]
Statistics and Computing, 2016 Monte Carlo methods represent the "de facto" standard for approximating complicated integrals involving multidimensional target distributions. In order to generate random realizations from the target distribution, Monte Carlo techniques use simpler proposal probability densities to draw candidate samples.
Martino, Luca +3 more
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Adaptive Multiple Importance Sampling [PDF]
Scandinavian Journal of Statistics, 2012 Abstract. The Adaptive Multiple Importance Sampling algorithm is aimed at an optimal recycling of past simulations in an iterated importance sampling (IS) scheme. The difference with earlier adaptive IS implementations like Population Monte Carlo is that the importance weights of all simulated values, past as well as present, are recomputed at each ...
Jean Marie Cornuet +3 more
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, 2023 Stein discrepancies have emerged as a powerful tool for retrospective improvement of Markov chain Monte Carlo output. However, the question of how to design Markov chains that are well-suited to such post-processing has yet to be addressed. This paper studies Stein importance sampling, in which weights are assigned to the states visited by a $Π ...
Wang, Congye +3 more
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Dual Free Adaptive Minibatch SDCA for Empirical Risk Minimization
Frontiers in Applied Mathematics and Statistics, 2018 In this paper we develop an adaptive dual free Stochastic Dual Coordinate Ascent (adfSDCA) algorithm for regularized empirical risk minimization problems. This is motivated by the recent work on dual free SDCA of Shalev-Shwartz [1].
Xi He, Rachael Tappenden, Martin Takáč
doaj +1 more source
Quantile estimation with adaptive importance sampling [PDF]
, 2010 We introduce new quantile estimators with adaptive importance sampling. The adaptive estimators are based on weighted samples that are neither independent nor identically distributed.
Egloff, Daniel, Leippold, Markus
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Heretical Multiple Importance Sampling [PDF]
IEEE Signal Processing Letters, 2016 Multiple Importance Sampling (MIS) methods approximate moments of complicated distributions by drawing samples from a set of proposal distributions. Several ways to compute the importance weights assigned to each sample have been recently proposed, with the so-called deterministic mixture (DM) weights providing the best performance in terms of variance,
Elvira, Víctor +3 more
openaire +5 more sources
Importance Sampling: Intrinsic Dimension and Computational Cost [PDF]
, 2017 The basic idea of importance sampling is to use independent samples from a proposal measure in order to approximate expectations with respect to a target measure.
Agapiou, S. +3 more
core +3 more sources