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» Input Modeling Using Quantile Statistical Methods
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TCAD
2010
164views more  TCAD 2010»
13 years 2 months ago
Advanced Variance Reduction and Sampling Techniques for Efficient Statistical Timing Analysis
The Monte-Carlo (MC) technique is a traditional solution for a reliable statistical analysis, and in contrast to probabilistic methods, it can account for any complicate model. How...
Javid Jaffari, Mohab Anis
NN
2006
Springer
163views Neural Networks» more  NN 2006»
13 years 7 months ago
Machine learning approaches for estimation of prediction interval for the model output
A novel method for estimating prediction uncertainty using machine learning techniques is presented. Uncertainty is expressed in the form of the two quantiles (constituting the pr...
Durga L. Shrestha, Dimitri P. Solomatine
WSC
2004
13 years 9 months ago
Bayesian Methods for Discrete Event Simulation
Bayesian methods are now used in a variety of ways in discrete-event simulation. Applications include input modeling, response surface modeling, uncertainty analysis, and experime...
Stephen E. Chick
WSC
2004
13 years 9 months ago
An Importance Sampling Method for Portfolios of Credit Risky Assets
The distribution of possible future losses for a portfolio of credit risky corporate assets, such as bonds or loans, shows strongly asymmetric behavior and a fat tail as the conse...
William J. Morokoff
DAC
2006
ACM
14 years 8 months ago
Gain-based technology mapping for minimum runtime leakage under input vector uncertainty
The gain-based technology mapping paradigm has been successfully employed for finding minimum delay and minimum area mappings. However, existing gain-based technology mappers fail...
Ashish Kumar Singh, Murari Mani, Ruchir Puri, Mich...