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» Input Modeling Using Quantile Statistical Methods
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MANSCI
2011
13 years 2 months ago
Generating Ambiguity in the Laboratory
This article develops a method for drawing samples from which it is impossible to infer any quantile or moment of the underlying distribution. The method provides researchers with...
Jack Stecher, Timothy Shields, John Dickhaut
WSC
2001
13 years 9 months ago
Resampling methods for input modeling
Stochastic simulation models are used to predict the behavior of real systems whose components have random variation. The simulation model generates artificial random quantities b...
Russell R. Barton, Lee Schruben
LREC
2010
188views Education» more  LREC 2010»
13 years 9 months ago
How Large a Corpus Do We Need: Statistical Method Versus Rule-based Method
We investigate the impact of input data scale in corpus-based learning using a study style of Zipf's law. In our research, Chinese word segmentation is chosen as the study ca...
Hai Zhao, Yan Song, Chunyu Kit
NIPS
2004
13 years 9 months ago
Support Vector Classification with Input Data Uncertainty
This paper investigates a new learning model in which the input data is corrupted with noise. We present a general statistical framework to tackle this problem. Based on the stati...
Jinbo Bi, Tong Zhang
KBSE
2010
IEEE
13 years 6 months ago
A bounded statistical approach for model checking of unbounded until properties
We study the problem of statistical model checking of probabilistic systems for PCTL unbounded until property P1p(ϕ1 U ϕ2) (where 1 ∈ {<, ≤, >, ≥}) using the computa...
Ru He, Paul Jennings, Samik Basu, Arka P. Ghosh, H...