Monte Carlo analysis of local distribution of negative refractive index in nanosphere-doped liquid crystal metamaterial

2009 ◽  
Author(s):  
M. Jarema ◽  
W. Walasik ◽  
G. Pawlik ◽  
R. Orlik ◽  
A. C. Mitus
Keyword(s):  
Monte Carlo ◽  
Refractive Index ◽  
Liquid Crystal ◽  
Negative Refractive Index ◽  
Monte Carlo Analysis ◽  
Local Distribution

Negative Refractive Index Liquid Crystal at Mid-Infrared Wavelength

2013 ◽  
Vol 28 (1) ◽  
pp. 15-18
Author(s):  
李晓平 LI Xiao-ping ◽  
贾丹 JIA Dan ◽  
杨程亮 YANG Cheng-liang ◽  
彭增辉 PENG Zeng-hui ◽  
宣丽 XUAN Li
Keyword(s):  
Refractive Index ◽  
Liquid Crystal ◽  
Negative Refractive Index ◽  
Mid Infrared ◽  
Infrared Wavelength

Large gradients of refractive index in nanosphere dispersed liquid crystal metamaterial with inhomogeneous anchoring: Monte Carlo study

2011 ◽  
Vol 33 (9) ◽  
pp. 1459-1463 ◽  
Author(s):  
Grzegorz Pawlik ◽  
Wiktor Walasik ◽  
Antoni C. Mitus ◽  
Iam Choon Khoo
Keyword(s):  
Monte Carlo ◽  
Refractive Index ◽  
Liquid Crystal ◽  
Monte Carlo Study

Generation of Correlated Logistic-Normal Random Variates for Medical Decision Trees

1998 ◽  
Vol 37 (03) ◽  
pp. 235-238 ◽  
Author(s):  
M. El-Taha ◽  
D. E. Clark
Keyword(s):  
Monte Carlo ◽  
Decision Trees ◽  
Computer Algebra ◽  
Taylor Series ◽  
Computer Algebra System ◽  
Random Variable ◽  
Monte Carlo Analysis ◽  
Normal Random Variable ◽  
Medical Decision ◽  
Theoretical Results

AbstractA Logistic-Normal random variable (Y) is obtained from a Normal random variable (X) by the relation Y = (ex)/(1 + ex). In Monte-Carlo analysis of decision trees, Logistic-Normal random variates may be used to model the branching probabilities. In some cases, the probabilities to be modeled may not be independent, and a method for generating correlated Logistic-Normal random variates would be useful. A technique for generating correlated Normal random variates has been previously described. Using Taylor Series approximations and the algebraic definitions of variance and covariance, we describe methods for estimating the means, variances, and covariances of Normal random variates which, after translation using the above formula, will result in Logistic-Normal random variates having approximately the desired means, variances, and covariances. Multiple simulations of the method using the Mathematica computer algebra system show satisfactory agreement with the theoretical results.

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