Results 11 to 20 of about 893,007 (263)
ANALYSIS OF DOSE RATES AROUND THE SLOVENIAN SILO-TYPE LILW REPOSITORY USING ADVANTG [PDF]
EPJ Web of Conferences, 2021 The ADVANTG code was used to analyze dose rates from the proposed Slovenian silo-type low and intermediate level waste (LILW) repository. Detailed calculations of dose rates are challenging as gamma-sources are located in thick concrete containers and ...
Kotnik Domen +3 more
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Statistical Theory and Related Fields, 2023 This paper deals with the Monte Carlo Simulation in a Bayesian framework. It shows the importance of the use of Monte Carlo experiments through refined descriptive sampling within the autoregressive model $ X_{t}=\rho X_{t-1}+Y_{t} $ , where $ 0 \lt \rho
Djoweyda Ghouil, Megdouda Ourbih-Tari
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Improving Random Projections With Extra Vectors to Approximate Inner Products
IEEE Access, 2020 This research concerns itself with increasing the accuracy of random projections used to quickly approximate the inner products of data vectors from a given dataset by adding additional information, namely, adding and storing more extra known vectors to ...
Yulong Li +3 more
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A Flexible Stochastic Multi-Agent ADMM Method for Large-Scale Distributed Optimization
IEEE Access, 2022 While applying stochastic alternating direction method of multiplier (ADMM) methods has become enormously potential in distributed applications, improving the algorithmic flexibility can bring huge benefits.
Lin Wu, Yongbin Wang, Tuo Shi
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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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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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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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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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