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Showing 1 to 8 of 8 for “"control variates"”.
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Combined correlation induction strategies for designed simulation experiments
… of the estimators of interest through a controlled laboratory-like simulation experiment. This research concentrates on correlation methods of VRTs which include common random numbers, antithetic variates and control variates. The basic idea of these methods is to utilize the linear …
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Low variance methods for Monte Carlo simulation of phonon transport
… develop such a code. Then, we use the concept of control variates in order to introduce the notion of deviational particles. Noticing that a thermalized system at equilibrium is inherently a solution of the Boltzmann Transport Equation, we take advantage of this deterministic piece of information: …
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Pricing swaptions on amortising swaps
… model, a variance reduction method, namely the control variates technique and a method of using deterministic low-discrepancy sequences (also called quasi-Monte Carlo methods).
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Learning-accelerated algorithms for simulation and optimization
… reduction algorithm, we develop an adaptive-control-variates technique for a class of simulations, where many particles interact via common mean fields. Due to the presence of a large number of particles and highly nonlinear dynamics, simulating these mean-field particle models is often …
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Variance reduction techniques for estimating quantiles and value-at-risk
… (IS), stratified sampling (SS), antithetic variates (AV), and control variates (CV). The method of proving the asymptotic validity was to first show that the quantile estimators obtained with VRTs satisfies a Bahadur-Ghosh representation. Then this was employed to prove central limit …
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Large-scale Bayesian computation using Stochastic Gradient Markov Chain Monte Carlo
… the dataset size. In light of this, we show how control variates can be used to develop an SGMCMC algorithm of $O(1)$, subject to two one-off preprocessing steps which each require a single pass through the dataset. While SGMCMC has gained significant popularity in the machine learning community, …
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Multifidelity Covariance Estimation Three Ways
… what we refer to as the Euclidean or linear control variate mutifidelity covariance estimator. The mean squared error of this estimator is available in closed form, which enables analytic optimization of sample allocations and weights to minimize expected squared Frobenius error subject to …
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Advancements in Monte Carlo many body methods
… improvements to the MC-MB family of methods. (5) Control variates are applied to the MC-MP family of methods. The application of these control variance produces speedups of 13.9, 17.11, and 58.29 for MC-MP2, MC-MP3, and MC-MP4, respectively. Of these algorithms, the first is apart of the seminal …