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...
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...
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...
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...
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...