Results 231 to 240 of about 109,420 (245)
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Adaptive importance sampling (digital communication)
IEEE Journal on Selected Areas in Communications, 1993Parametric adaptive importance sampling (IS) algorithms that adapt the IS density to the system of interest during the course of the simulation are discussed. This approach removes the burden of selecting the IS density from the system designer. The performance of two such algorithms is investigated for both linear and nonlinear systems operating in ...
J.S. Stadler, S. Roy
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Table‐driven Adaptive Importance Sampling
Computer Graphics Forum, 2008AbstractMonte Carlo rendering algorithms generally rely on some form of importance sampling to evaluate the measurement equation. Most of these importance sampling methods only take local information into account, however, so the actual importance function used may not closely resemble the light distribution in the scene.
David Cline, Daniel Adams, Parris Egbert
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Adaptive Importance Latin Hypercube Sampling
Volume 6: Turbo Expo 2003, Parts A and B, 2003Probabilistic methods currently require many function evaluations or do not provide a mathematically robust confidence interval. The proposed method searches to find the Most Probable Point (MPP) using a Hasofer-Lind-Rackwitz-Fiessler (HLRF) algorithm, and then estimates reliability with Latin Hypercube Sampling (LHS) evaluating only those points ...
Brian K. Beachkofski, Ramana V. Grandhi
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Unsupervised Domain Adaptation via Importance Sampling
IEEE Transactions on Circuits and Systems for Video Technology, 2020Unsupervised domain adaptation aims to generalize a model from the label-rich source domain to the unlabeled target domain. Existing works mainly focus on aligning the global distribution statistics between source and target domains. However, they neglect distractions from the unexpected noisy samples in domain distribution estimation, leading to ...
Xuemiao Xu +4 more
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Optimizing Adaptive Importance Sampling by Stochastic Approximation
SIAM Journal on Scientific Computing, 2018zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Adaptive importance sampling and control variates
Journal of Mathematical Analysis and Applications, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Adaptive Importance Sampling for simulating copula-based distributions
Insurance: Mathematics and Economics, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Acceleration on Adaptive Importance Sampling with Sample Average Approximation
SIAM Journal on Scientific Computing, 2017Summary: We construct and analyze acceleration techniques for adaptive Monte Carlo simulations for general multivariate probability laws when the sample average approximation is employed for optimal parameter search. Our goal is to accelerate the adaptive Monte Carlo estimation by leading the parameter search line based on the sample average ...
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Adaptive Importance Sampling with a Rapidly Varying Importance Function
Nuclear Science and Engineering, 2000It is known well that zero-variance Monte Carlo solutions are possible if an exact importance function is available to bias the random walks. Monte Carlo can be used to estimate the importance function. This estimated importance function then can be used to bias a subsequent Monte Carlo calculation that estimates an even better importance function ...
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MCMC-Driven Adaptive Multiple Importance Sampling
2015Monte Carlo (MC) methods are widely used for statistical inference and stochastic optimization. A well-known class of MC methods is composed of importance sampling (IS) and its adaptive extensions (such as adaptive multiple IS and population MC). In this work, we introduce an iterated batch importance sampler using a population of proposal densities ...
Luca Martino +3 more
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