Experimental comparison of Monte-Carlo sampling techniques to evaluate the multivariate normal integral.

1969 ◽  
Author(s):  
Elizabeth N. Abbe
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Sampling ◽  
Experimental Comparison ◽  
Sampling Techniques ◽  
Multivariate Normal ◽  
Multivariate Normal Integral

scholarly journals Generating macroscopic, purpose-dependent trips through Monte Carlo sampling techniques

2017 ◽  
Vol 27 ◽  
pp. 585-592 ◽  
Author(s):  
Ariane Scheffer ◽  
Guido Cantelmo ◽  
Francesco Viti
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Sampling ◽  
Sampling Techniques

Monte Carlo Sampling Techniques

2002 ◽  
pp. 21-56
Author(s):  
Stephen A. Dupree ◽  
Stanley K. Fraley
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Sampling ◽  
Sampling Techniques

A COMBINATION OF REPLICA EXCHANGE MONTE CARLO AND ENERGY LANDSCAPE PAVING ALGORITHMS TO INCREASE THE EFFECTIVENESS OF CONFORMATIONAL SAMPLING

2004 ◽  
Vol 15 (07) ◽  
pp. 933-937 ◽  
Author(s):  
HANDAN ARKIN
Keyword(s):  
Monte Carlo ◽  
Hybrid Algorithm ◽  
Energy Landscape ◽  
Monte Carlo Sampling ◽  
Replica Exchange ◽  
Conformational Sampling ◽  
Sampling Techniques ◽  
Replica Exchange Monte Carlo ◽  
Energy Landscape Paving ◽  
Low Energy Conformations

A combination of replica exchange Monte Carlo sampling techniques and energy landscape paving approach is presented. This hybrid algorithm combines the features of the energy landscape paving (ELP) and replica exchange methods (REM). I have tested its performance in studying the low-energy conformations of the benchmark peptide Met-enkephalin.

Comments on Adjoint Monte Carlo Sampling Techniques

1970 ◽  
Vol 42 (1) ◽  
pp. 118-118
Author(s):  
Leo B. Levitt ◽  
Jerome Spanier
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Sampling ◽  
Sampling Techniques

Monte Carlo Sampling Techniques

2004 ◽  
pp. 13-25
Author(s):  
Stephen A. Dupree ◽  
Stanley K. Fraley
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Sampling ◽  
Sampling Techniques

scholarly journals Identification of Efficient Sampling Techniques for Probabilistic Voltage Stability Analysis of Renewable-Rich Power Systems

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2328
Author(s):  
Mohammed Alzubaidi ◽  
Kazi N. Hasan ◽  
Lasantha Meegahapola ◽  
Mir Toufikur Rahman
Keyword(s):  
Monte Carlo ◽  
Stability Analysis ◽  
Power Systems ◽  
Large Scale ◽  
Voltage Stability ◽  
Sampling Technique ◽  
Coefficient Of Determination ◽  
Sampling Techniques ◽  
Quasi Monte Carlo ◽  
Efficient Sampling

This paper presents a comparative analysis of six sampling techniques to identify an efficient and accurate sampling technique to be applied to probabilistic voltage stability assessment in large-scale power systems. In this study, six different sampling techniques are investigated and compared to each other in terms of their accuracy and efficiency, including Monte Carlo (MC), three versions of Quasi-Monte Carlo (QMC), i.e., Sobol, Halton, and Latin Hypercube, Markov Chain MC (MCMC), and importance sampling (IS) technique, to evaluate their suitability for application with probabilistic voltage stability analysis in large-scale uncertain power systems. The coefficient of determination (R2) and root mean square error (RMSE) are calculated to measure the accuracy and the efficiency of the sampling techniques compared to each other. All the six sampling techniques provide more than 99% accuracy by producing a large number of wind speed random samples (8760 samples). In terms of efficiency, on the other hand, the three versions of QMC are the most efficient sampling techniques, providing more than 96% accuracy with only a small number of generated samples (150 samples) compared to other techniques.

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