scholarly journals Monte Carlo model and variance reduction method based on lidar of ship wake

2013 ◽  
Vol 62 (1) ◽  
pp. 015205
Author(s):  
Liang Shan-Yong ◽  
Wang Jiang-An ◽  
Zhang Feng ◽  
Wu Rong-Hua ◽  
Zong Si-Guang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Variance Reduction ◽  
Reduction Method ◽  
Monte Carlo Model ◽  
Ship Wake

scholarly journals Stratification as a General Variance Reduction Method for Markov Chain Monte Carlo

2020 ◽  
Vol 8 (3) ◽  
pp. 1139-1188
Author(s):  
Aaron R. Dinner ◽  
Erik H. Thiede ◽  
Brian Van Koten ◽  
Jonathan Weare
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Variance Reduction ◽  
Reduction Method

Global variance reduction method based on multi-group Monte Carlo adjoint calculation

2021 ◽  
Vol 151 ◽  
pp. 107958
Author(s):  
Tao Shi ◽  
Hui Li ◽  
Qianxue Ding ◽  
Mengqi Wang ◽  
Zheng Zheng ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Variance Reduction ◽  
Reduction Method ◽  
Global Variance Reduction

Computational Cost and Accuracy in Calculating Three-Dimensional Radiative Transfer: Results for New Implementations of Monte Carlo and SHDOM

2009 ◽  
Vol 66 (10) ◽  
pp. 3131-3146 ◽  
Author(s):  
Robert Pincus ◽  
K. Franklin Evans
Keyword(s):  
Monte Carlo ◽  
Radiative Transfer ◽  
Variance Reduction ◽  
Parallel Implementation ◽  
Monte Carlo Model ◽  
Computational Cost ◽  
Three Dimensional ◽  
Computation Time ◽  
Flux Divergence ◽  
Pixel Intensity

Abstract This paper examines the tradeoffs between computational cost and accuracy for two new state-of-the-art codes for computing three-dimensional radiative transfer: a community Monte Carlo model and a parallel implementation of the Spherical Harmonics Discrete Ordinate Method (SHDOM). Both codes are described and algorithmic choices are elaborated. Two prototype problems are considered: a domain filled with stratocumulus clouds and another containing scattered shallow cumulus, absorbing aerosols, and molecular scatterers. Calculations are performed for a range of resolutions and the relationships between accuracy and computational cost, measured by memory use and time to solution, are compared. Monte Carlo accuracy depends primarily on the number of trajectories used in the integration. Monte Carlo estimates of intensity are computationally expensive and may be subject to large sampling noise from highly peaked phase functions. This noise can be decreased using a range of variance reduction techniques, but these techniques can compromise the excellent agreement between the true error and estimates obtained from unbiased calculations. SHDOM accuracy is controlled by both spatial and angular resolution; different output fields are sensitive to different aspects of this resolution, so the optimum accuracy parameters depend on which quantities are desired as well as on the characteristics of the problem being solved. The accuracy of SHDOM must be assessed through convergence tests and all results from unconverged solutions may be biased. SHDOM is more efficient (i.e., has lower error for a given computational cost) than Monte Carlo when computing pixel-by-pixel upwelling fluxes in the cumulus scene, whereas Monte Carlo is more efficient in computing flux divergence and downwelling flux in the stratocumulus scene, especially at higher accuracies. The two models are comparable for downwelling flux and flux divergence in cumulus and upwelling flux in stratocumulus. SHDOM is substantially more efficient when computing pixel-by-pixel intensity in multiple directions; the models are comparable when computing domain-average intensities. In some cases memory use, rather than computation time, may limit the resolution of SHDOM calculations.

Global variance reduction method for global Monte Carlo particle transport simulations of CFETR

2017 ◽  
Vol 28 (8) ◽  
Author(s):  
Xing-Chen Nie ◽  
Jia Li ◽  
Song-Lin Liu ◽  
Xiao-Kang Zhang ◽  
Ping-Hui Zhao ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Particle Transport ◽  
Variance Reduction ◽  
Reduction Method ◽  
Global Variance Reduction

scholarly journals ANALISIS RISIKO PORTOFOLIO MENGGUNAKAN METODE SIMULASI MONTE CARLO CONTROL VARIATES

2021 ◽  
Vol 10 (4) ◽  
pp. 192
Author(s):  
IRENE MAYLINDA PANGARIBUAN ◽  
KOMANG DHARMAWAN ◽  
I WAYAN SUMARJAYA
Keyword(s):  
At Risk ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Value At Risk ◽  
Variance Reduction ◽  
Reduction Method ◽  
Random Numbers ◽  
Control Variates ◽  
Popular Method ◽  
Maximum Loss

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. The purpose of this study is to demonstrate control variates method as a variance reduction method that can be applied to estimate VaR. Moreover, it is to compare the results with the normal VaR method or analytical VaR calculation. Control variates method was used to find new returns from all stocks which are used as estimators of the control variates. The new returns were then used to define parameters needed to generate N random numbers. Furthermore, the generated numbers were used to find the VaR value. The method was then applied to estimate a portfolio of the game and esports company stocks that are EA, TTWO, AESE, TCEHY, and ATVI . The results show Monte Carlo simulation gives VaR of US$41.6428 within 1000 simulation, while the analytical VaR calculation  or  normal VaR method gives US$30.0949.

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