L 2 Model Reduction and Variance Reduction
IFAC Proceedings Volumes, 2000 The authors study several variance properties related to model reduction for finite impulse response (FIR) and output error (OE) models. The variances of two models, one deduced directly from data and the other by reducing a high order model by \(L_2\) model reduction, are compared.
Fredrik Tjärnström, Lennart Ljung
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A Generative Adversarial Network Approach to Calibration of Local Stochastic Volatility Models
Risks, 2020 We propose a fully data-driven approach to calibrate local stochastic volatility (LSV) models, circumventing in particular the ad hoc interpolation of the volatility surface.
Christa Cuchiero +2 more
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Choosing Transport Events for Initiating Splitting and Rouletting
Journal of Nuclear Engineering, 2021 A study was performed to determine which transport events should be used to initiate a weight window lookup to achieve the best variance reduction performance.
Evan S. Gonzalez, Gregory G. Davidson
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INVESTIGATION ON DETERMINISTIC TRUNCATION TO CONTINUOUS ENERGY MONTE CARLO NEUTRON TRANSPORT CALCULATION [PDF]
EPJ Web of Conferences, 2021 This paper presents the application and evaluation of a deterministic truncation of Monte Carlo (DTMC) solution method in a whole core reactor problem based on a continuous energy transport calculation. The DTMC method has been studied and developed as a
Kim Inhyung, Kim Yonghee
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Variance Reduction with Sparse Gradients
CoRR, 2020 Variance reduction methods such as SVRG and SpiderBoost use a mixture of large and small batch gradients to reduce the variance of stochastic gradients. Compared to SGD, these methods require at least double the number of operations per update to model parameters.
Melih Elibol +2 more
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Variance Reduction of Sequential Monte Carlo Approach for GNSS Phase Bias Estimation
Mathematics, 2020 Global navigation satellite systems (GNSS) are an important tool for positioning, navigation, and timing (PNT) services. The fast and high-precision GNSS data processing relies on reliable integer ambiguity fixing, whose performance depends on phase bias
Yumiao Tian, Maorong Ge, Frank Neitzel
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IMPLEMENTATION OF MONTE CARLO MOMENT MATCHING METHOD FOR PRICING LOOKBACK FLOATING STRIKE OPTION
Barekeng, 2022 Monte Carlo method was a numerical method that was popular in finance. This method had disadvantages at convergences, so the moment matching was used to improve the efficiency from Monte Carlo method.
Komang Nonik Afsari Dewi +2 more
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Generalizing the Balance Heuristic Estimator in Multiple Importance Sampling
Entropy, 2022 In this paper, we propose a novel and generic family of multiple importance sampling estimators. We first revisit the celebrated balance heuristic estimator, a widely used Monte Carlo technique for the approximation of intractable integrals.
Mateu Sbert, Víctor Elvira
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Variance reduction methods [PDF]
Proceedings of the 18th conference on Winter simulation - WSC '86, 1986 A computer simulation model is unusual in that the random error is under the total control of the experimenter. Variance reduction methods aim to take advantage of this to improve experimental accuracy. The fundamental ideas behind the most important of these methods will be described and illustrated with simple examples.
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A Neutrosophic Monte Carlo Framework for Modeling Indeterminate Participation and Cultural Impact in Tourism Service Quality of Ethnic Sports Events [PDF]
Neutrosophic Sets and SystemsEthnic sports tourism involves complex cultural, social, and economic interactions, where uncertainty arises not only from randomness but also from incomplete and contradictory information. Classical probability models cannot fully capture these features.
Chaolumen Ge, Xuelian Liu
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