An efficient method for treating conditional Monte Carlo simulation

1994 ◽  
Vol 83 (2-3) ◽  
pp. 147-155
Author(s):  
Hai-Ping Fang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Efficient Method ◽  
Conditional Monte Carlo

scholarly journals Volume determination of weights in the range from 1 g to 5 kg: a comparison of hydrostatic weighing and double-weighing in air using Monte-Carlo simulation

ACTA IMEKO ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 61
Author(s):  
Yi Su ◽  
Kilian Marti ◽  
Christian Wuethrich
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Efficient Method ◽  
Volume Determination ◽  
Correlated Variables ◽  
Hydrostatic Weighing ◽  
Mathematical Equations ◽  
Measurement Uncertainties

We have investigated the two methods, double-weighing in air and hydrostatic weighing, for the determination of the volume of weights in the range from 5 kg down to 1 g. We present the mathematical equations of both methods and show that Monte-Carlo simulation is a suitable way to determine the measurement uncertainties and overcome the difficulties in dealing with correlated variables. We found that the measurement uncertainties of the two methods are comparable and that double-weighing in air is an efficient method for determining the volume of weights below 1 kg.

scholarly journals Quantiles on Stream: An Application to Monte Carlo Simulation

2016 ◽  
Vol 4 (4) ◽  
pp. 334-342 ◽  
Author(s):  
Wei Wang ◽  
Wai-Ki Ching ◽  
Shouyang Wang ◽  
Lean Yu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Theoretical Analysis ◽  
Sample Size ◽  
Efficient Method ◽  
Numerical Example ◽  
Asymptotically Normal ◽  
Limited Memory ◽  
Asymptotically Normal Estimator

AbstractMonte Carlo simulation is an efficient method to estimate quantile. However, it becomes a serious problem when a huge sample size is required but the memory is insufficient. In this paper, we apply the stream quantile algorithm to Monte Carlo simulation in order to estimate quantile with limited memory. A rigorous theoretical analysis on the properties of theϵn-approximate quantile is proposed in this paper. We prove that ifϵn=o(n-1/2), then theϵn-approximateα-quantile computed by any deterministic stream quantile algorithm is a consistent and asymptotically normal estimator of the true quantileqα. We suggest settingϵn= 1/(n1/2log10n) in practice. Two deterministic stream quantile algorithms, including of GK algorithm and ZW algorithm, are employed to illustrate the performance of theϵn-approximate quantile. The numerical example shows that the deterministic stream quantile algorithm can provide desired estimator of the true quantile with less memory.

PARALLEL WOLFF CLUSTER ALGORITHMS

1995 ◽  
Vol 06 (02) ◽  
pp. 197-210 ◽  
Author(s):  
S. BAE ◽  
S.H. KO ◽  
P.D. CODDINGTON
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Efficient Method ◽  
Cluster Algorithm ◽  
Parallel Implementation ◽  
Parallel Computer ◽  
Spin Models ◽  
Cluster Algorithms ◽  
Vector Machines ◽  
Single Cluster

The Wolff single-cluster algorithm is the most efficient method known for Monte Carlo simulation of many spin models. Due to the irregular size, shape and position of the Wolff clusters, this method does not easily lend itself to efficient parallel implementation, so that simulations using this method have thus far been confined to workstations and vector machines. Here we present two parallel implementations of this algorithm, and show that one gives fairly good performance on a MIMD parallel computer.

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