Results 11 to 20 of about 109,420 (245)
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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Hamiltonian Adaptive Importance Sampling [PDF]
IEEE Signal Processing Letters, 2021 Importance sampling (IS) is a powerful Monte Carlo (MC) methodology for approximating integrals, for instance in the context of Bayesian inference. In IS, the samples are simulated from the so-called proposal distribution, and the choice of this proposal is key for achieving a high performance.
Ali Mousavi, Reza Monsefi, Victor Elvira
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Adaptive importance sampling for network growth models. [PDF]
Ann Oper Res, 2011 Network Growth Models such as Preferential Attachment and Duplication/Divergence are popular generative models with which to study complex networks in biology, sociology, and computer science. However, analyzing them within the framework of model selection and statistical inference is often complicated and computationally difficult, particularly when ...
Guetz AN, Holmes SP.
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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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Ensemble Transport Adaptive Importance Sampling [PDF]
SIAM/ASA Journal on Uncertainty Quantification, 2019 Markov chain Monte Carlo methods are a powerful and commonly used family of numerical methods for sampling from complex probability distributions. As applications of these methods increase in size and complexity, the need for efficient methods increases. In this paper, we present a particle ensemble algorithm.
Colin Cotter, Simon Cotter, Paul Russell
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Quantile Estimation with Adaptive Importance Sampling [PDF]
SSRN Electronic Journal, 2007 We introduce new quantile estimators with adaptive importance sampling. The adaptive estimators are based on weighted samples that are neither independent nor identically distributed. Using a new law of iterated logarithm for martingales, we prove the convergence of the adaptive quantile estimators for general distributions with nonunique quantiles ...
Leippold, Markus, Egloff, Daniel
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Robust Covariance Adaptation in Adaptive Importance Sampling [PDF]
IEEE Signal Processing Letters, 2018 Importance sampling (IS) is a Monte Carlo methodology that allows for approximation of a target distribution using weighted samples generated from another proposal distribution. Adaptive importance sampling (AIS) implements an iterative version of IS which adapts the parameters of the proposal distribution in order to improve estimation of the target ...
Yousef El-Laham +2 more
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Mathematics, 2023 Due to industrial demands to handle increasing amounts of training data, lower the cost of computing one model at a time, and lessen the ecological effects of intensive computing resource consumption, the job of speeding the training of deep neural ...
Alexander Demidovskij +5 more
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Efficient Adaptive Multiple Importance Sampling [PDF]
2019 27th European Signal Processing Conference (EUSIPCO), 2019 The adaptive multiple importance sampling (AMIS) algorithm is a powerful Monte Carlo tool for Bayesian estimation in intractable models. The uniqueness of this methodology from other adaptive importance sampling (AIS) schemes is in the weighting procedure, where at each iteration of the algorithm, all samples are re-weighted according to the temporal ...
El-Laham, Yousef +3 more
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