Efficient non-Monte Carlo method for statistical analysis of periodically switched linear circuits in frequency domain

2003 ◽  
Vol 150 (5) ◽  
pp. 423
Author(s):  
F. Yuan
Keyword(s):  
Monte Carlo ◽  
Statistical Analysis ◽  
Monte Carlo Method ◽  
Frequency Domain ◽  
Linear Circuits

scholarly journals Time- and Frequency-Domain Evaluation of Stochastic Parameters on Signal Lines

2012 ◽  
Vol 1 (3) ◽  
pp. 85 ◽  
Author(s):  
P. Manfredi ◽  
I. S. Stievano ◽  
F. G. Canavero
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Frequency Domain ◽  
Process Variations ◽  
Response Sensitivity ◽  
Enhanced Transmission ◽  
Stochastic Parameters ◽  
Telegraph Equations ◽  
Transmission Line Models ◽  
Alternative Solutions

This paper focuses on the derivation of enhanced transmission-line models allowing to describe, in time and frequency domain, a realistic interconnect with the inclusion of external uncertainties, like process variations or routing and layout uncertainties. The proposed method, that is based on the expansion of the well-known telegraph equations in terms of orthogonal polynomials, turns out to be accurate and more efficient than alternative solutions like Monte Carlo method in determining the transmissionline response sensitivity to parameters variability. Moreover, an implementation into standard circuit analysis tools such as SPICE is possible. Two application examples based on PCB structures of common use in commercial packages conclude the paper.

scholarly journals A new Monte Carlo method for neutron noise calculations in the frequency domain

2017 ◽  
Vol 102 ◽  
pp. 465-475 ◽  
Author(s):  
Amélie Rouchon ◽  
Andrea Zoia ◽  
Richard Sanchez
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Frequency Domain ◽  
Neutron Noise

scholarly journals Error synthesis and statistical analysis on stepped mirror array by Monte Carlo method

2012 ◽  
Vol 61 (22) ◽  
pp. 220701
Author(s):  
Lyu Jin-Guang ◽  
Liang Jing-Qiu ◽  
Liang Zhong-Zhu
Keyword(s):  
Monte Carlo ◽  
Statistical Analysis ◽  
Monte Carlo Method

Frequency-domain deviational Monte Carlo method for linear oscillatory gas flows

2015 ◽  
Vol 27 (10) ◽  
pp. 102002 ◽  
Author(s):  
Daniel R. Ladiges ◽  
John E. Sader
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
Frequency Domain ◽  
Gas Flows

scholarly journals Statistical Analysis Of A Class: Monte Carlo And Multiple Imputation Spreadsheet Methods For Estimation And Extrapolation

2017 ◽  
Vol 10 (2) ◽  
pp. 81-96
Author(s):  
Laurel J. Fish ◽  
Dennis Halcoussis ◽  
G. Michael Phillips
Keyword(s):  
Monte Carlo ◽  
Statistical Analysis ◽  
Monte Carlo Method ◽  
Multiple Imputation ◽  
Monte Carlo Methods ◽  
Small Samples ◽  
Business Professionals ◽  
The Monte Carlo Method ◽  
Sample Characteristics ◽  
Analyze Data

The Monte Carlo method and related multiple imputation methods are traditionally used in math, physics and science to estimate and analyze data and are now becoming standard tools in analyzing business and financial problems.  However, few sources explain the application of the Monte Carlo method for individuals and business professionals who are not immersed in the realm of mathematics or science.  This paper introduces these Monte Carlo methods for the non-mathematician and business student, providing examples where the Monte Carlo method is applied when only small samples are available.  Statistical analysis and statistically sound extrapolation of sample characteristics to the larger class population can be facilitated by applying Monte Carlo methods and the related concept of multiple imputation, which is also explained.  Appendices provide step-by-step instructions for using two popular spreadsheet add-ins to run Monte Carlo based analysis.

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