Results 21 to 30 of about 17,312,778 (333)
Barekeng, 2023 In this paper, we applied the standard Monte Carlo, antithetic variate, and control variates methods to value the double barrier knock-in option price.
Romaito Br Silalahi +2 more
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Multifidelity Ensemble Kalman Filtering Using Surrogate Models Defined by Theory-Guided Autoencoders
Frontiers in Applied Mathematics and Statistics, 2022 Data assimilation is a Bayesian inference process that obtains an enhanced understanding of a physical system of interest by fusing information from an inexact physics-based model, and from noisy sparse observations of reality. The multifidelity ensemble
Andrey A. Popov, Adrian Sandu
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Recursive Control Variates for Inverse Rendering
ACM Transactions on Graphics, 2023 We present a method for reducing errors---variance and bias---in physically based differentiable rendering (PBDR). Typical applications of PBDR repeatedly render a scene as part of an optimization loop involving gradient descent.
Baptiste Nicolet +5 more
semanticscholar +1 more source
ANALISIS RISIKO PORTOFOLIO MENGGUNAKAN METODE SIMULASI MONTE CARLO CONTROL VARIATES
E-Jurnal Matematika, 2021 Value at Risk (VaR) is a method to measure the maximum loss with a certain level of confidence in a certain period. Monte Carlo simulation is the most popular method of calculating VaR.
IRENE MAYLINDA PANGARIBUAN +2 more
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Adaptive Testing for Connected and Automated Vehicles with Sparse Control Variates in Overtaking Scenarios [PDF]
2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), 2022 Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs). Due to the black-box property and various types of CAVs, how to test and evaluate CAVs adaptively remains a major challenge.
Jingxuan Yang +4 more
semanticscholar +1 more source
Multifidelity Reinforcement Learning with Control Variates [PDF]
arXiv.org, 2022 In many computational science and engineering applications, the output of a system of interest corresponding to a given input can be queried at different levels of fidelity with different costs.
Sami Khairy, Prasanna Balaprakash
semanticscholar +1 more source
Adaptive Control Variates [PDF]
Proceedings of the 2004 Winter Simulation Conference, 2004., 2005 Adaptive Control ...
Kim, S., Henderson, S. G.
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Control of Variation by Reward Probability. [PDF]
Journal of Experimental Psychology: Animal Behavior Processes, 2004 Two bar-press experiments with rats tested the rule that reducing expectation of reward increases the variation from which reward selects. Experiment 1 used a discrete-trial random-interval schedule, with trials signaled by light or sound. One signal always ended with reward; the other signal ended with reward less often.
Gharib, A, Gade, C, Roberts, Seth
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Bayesian control variates for optimal covariance estimation with pairs of simulations and surrogates [PDF]
Monthly notices of the Royal Astronomical Society, 2022 Predictions of the mean and covariance matrix of summary statistics are critical for confronting cosmological theories with observations, not least for likelihood approximations and parameter inference.
Nicolas Chartier, Benjamin Dan Wandelt
semanticscholar +1 more source
Control Variates for Quantile Estimation [PDF]
Management Science, 1987 New point and interval estimators for quantiles that employ a control variate are introduced. The properties of these estimators do not depend on the usual assumption of joint normality between the random variable of interest and the control. Illustrative examples for queueing and stochastic activity network models are given.
Jason C. Hsu, Barry L. Nelson
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