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Monte Carlo and Quasi-Monte Carlo Methods
2020Monte Carlo is one of the most versatile and widely used numerical methods. Its convergence rate, O(N~1^2), is independent of dimension, which shows Monte Carlo to be very robust but also slow. This article presents an introduction to Monte Carlo methods for integration problems, including convergence theory, sampling methods and variance reduction ...
Tuffin, Bruno, L'Écuyer, Pierre
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GEM - International Journal on Geomathematics, 2017
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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The Monte Carlo method: Theory and computational issues
Thermal Radiation Heat Transfer, 2002. This paper consists of two independent parts. (1) The Monte Carlo method for computing the evolution of spherical stellar systems has been modified so that the computation can be continued after the time of formation of the central singularity. Results
John R. Howell +3 more
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Optical Engineering: The Journal of SPIE
. We proposed a polarized bidirectional reflectance distribution function (PBRDF) modeling method based on the Monte Carlo method to study the light reflection polarization characteristics of the coating surface.
Dong Zhixu +3 more
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. We proposed a polarized bidirectional reflectance distribution function (PBRDF) modeling method based on the Monte Carlo method to study the light reflection polarization characteristics of the coating surface.
Dong Zhixu +3 more
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Transactions of the Institute of Measurement and Control, 2020
The measurement of the centroid is of great significance to improve the control performance and reduce the energy consumption of the planetary rover (PR). The uncertainty is an essential indicator of the reliability of centroid measurement results.
Qiang Na +3 more
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The measurement of the centroid is of great significance to improve the control performance and reduce the energy consumption of the planetary rover (PR). The uncertainty is an essential indicator of the reliability of centroid measurement results.
Qiang Na +3 more
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Journal of the Air and Waste Management Association, 2021
Ömer Harun Özkaynak, G. T. İçemer
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Ömer Harun Özkaynak, G. T. İçemer
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Reliability Engineering & System Safety, 2010
To reduce cost of Monte Carlo (MC) simulations for time-consuming processes, Bayesian Monte Carlo (BMC) is introduced in this paper. The BMC method reduces number of realizations in MC according to the desired accuracy level. BMC also provides a possibility of considering more priors. In other words, different priors can be integrated into one model by
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To reduce cost of Monte Carlo (MC) simulations for time-consuming processes, Bayesian Monte Carlo (BMC) is introduced in this paper. The BMC method reduces number of realizations in MC according to the desired accuracy level. BMC also provides a possibility of considering more priors. In other words, different priors can be integrated into one model by
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2019
The sequential use of random numbers, to sample the values of probability variables, allows obtaining solutions to mathematical problems such as the Monte Carlo method, that allows to model stochastic parameters or deterministic based on random sampling.
Lorenzo Cevallos-Torres +1 more
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The sequential use of random numbers, to sample the values of probability variables, allows obtaining solutions to mathematical problems such as the Monte Carlo method, that allows to model stochastic parameters or deterministic based on random sampling.
Lorenzo Cevallos-Torres +1 more
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Realistic Availability Assessment of Energy System Behavior Patterns by Monte Carlo Method
The Arabian journal for science and engineering, 2021Mahyar Momen, A. Behbahaninia
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