Transformations for White Noise Functionals

Normal Approximation for White Noise Functionals by Stein’s Method and Hida Calculus

Limit theorems for nonlinear functionals of Volterra processes via white noise analysis

By means of white noise analysis, we prove some limit theorems for nonlinear functionals of a given Volterra process. In particular, our results apply to fractional Brownian motion (fBm), and should be compared with the classical convergence results of the eighties by Breuer, Dobrushin, Giraitis, Major, Surgailis and Taqqu, as well as the recent advances concerning the construction of a Lvy are...

Local Graph Transformations Driven by Lyapunov Functionals

We study the dynamical behavior of automata networks defined by x(t + 1) = x(t) + f(Ax(t) + b); where A is a symmetric n x n matrix, b is a real n-vector and f is the subgradient of a convex function . More precisely we prove, by using Lyapunov operators associated to the network, that the steady state behavior of these automata is simple: fixed points or two-cycles. We also give bounds for the...

White-Box Coverage Criteria for Model Transformations

Model transformations are core to MDE, and one of the key aspects for all model transformations is that they are validated. In this paper we develop an approach to testing model transformations based on white-box coverage measures of the transformations. To demonstrate the use of this approach we apply it to some examples from the ATL metamodel zoo.

White Functionals for Anomaly Detection in Dynamical Systems

We propose new methodologies to detect anomalies in discrete-time processes taking values in a probability space. These methods are based on the inference of functionals whose evaluations on successive states visited by the process are stationary and have low autocorrelations. Deviations from this behavior are used to flag anomalies. The candidate functionals are estimated in a subspace of a re...