Results 11 to 20 of about 1,302,044 (285)
Data-Centric Engineering, 2021 Monte Carlo algorithms simulates some prescribed number of samples, taking some random real time to complete the computations necessary. This work considers the converse: to impose a real-time budget on the computation, which results in the number of ...
Lawrence M. Murray +2 more
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JTCVS Open, 2021 Objectives: Despite coherent guidelines, management of functional tricuspid regurgitation (FTR) consequences on outcome in the context of degenerative mitral regurgitation (DMR) remains controversial due to lacking series of large magnitude with rigorous
Gilles D. Dreyfus, MD, PhD +7 more
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A HYBRID MONTE-CARLO-DETERMINISTIC METHOD FOR AP1000 EX-CORE DETECTOR RESPONSE SIMULATION [PDF]
EPJ Web of Conferences, 2021 The ex-core detector-response calculation is a typical deep-penetration problem, which is challenging for the Monte Carlo method. The response of the ex-core detector is an important parameter for the safe operation of the nuclear power plants. Meanwhile,
Zheng Qi +6 more
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Journal of Applied Probability, 2019 AbstractIn this paper we propose a new theory and methodology to tackle the problem of unifying Monte Carlo samples from distributed densities into a single Monte Carlo draw from the target density. This surprisingly challenging problem arises in many settings (for instance, expert elicitation, multiview learning, distributed ‘big data’ problems, etc.),
Dai, Hongsheng +2 more
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Journal of Turbulence, 2020 This paper explores how far the scientific discovery process can be automated. Using the identification of causally significant flow structures in two-dimensional turbulence as an example, it probes how far the usual procedure of planning experiments to test hypotheses can be substituted by `blind' randomised experiments, and notes that the increased ...
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Monte Carlo and Quasi–Monte Carlo Density Estimation via Conditioning [PDF]
INFORMS Journal on Computing, 2022 Estimating the unknown density from which a given independent sample originates is more difficult than estimating the mean in the sense that, for the best popular nonparametric density estimators, the mean integrated square error converges more slowly than at the canonical rate of [Formula: see text].
Pierre L’Ecuyer +2 more
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TRANSITION MATRIX MONTE CARLO [PDF]
International Journal of Modern Physics C, 1999 Although histogram methods have been extremely effective for analyzing data from Monte Carlo simulations, they do have certain limitations, including the range over which they are valid and the difficulties of combining data from independent simulations.
Swendsen, R.H. +5 more
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PERHITUNGAN VaR PORTOFOLIO SAHAM MENGGUNAKAN DATA HISTORIS DAN DATA SIMULASI MONTE CARLO
E-Jurnal Matematika, 2012 Value at Risk (VaR) is the maximum potential loss on a portfolio based on the probability at a certain time. In this research, portfolio VaR values calculated from historical data and Monte Carlo simulation data.
WAYAN ARTHINI +2 more
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Error in Monte Carlo, quasi-error in Quasi-Monte Carlo [PDF]
, 2005 While the Quasi-Monte Carlo method of numerical integration achieves smaller integration error than standard Monte Carlo, its use in particle physics phenomenology has been hindered by the abscence of a reliable way to estimate that error.
Achilleas Lazopoulos +13 more
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Efficient Monte Carlo Calculations of the One-Body Density [PDF]
, 2006 An alternative Monte Carlo estimator for the one-body density rho(r) is presented. This estimator has a simple form and can be readily used in any type of Monte Carlo simulation.
Assaraf, Roland +2 more
core +7 more sources