Sentence examples for measures for stochastic from inspiring English sources

In this paper, we first define some network measures for stochastic graphs, and then we propose four sampling algorithms based on learning automata for stochastic graphs.

The first study of dynamic risk measures for stochastic processes (finite probability space and discrete time) is attributed to Riedel (2004), where the author introduced the (strong) time consistency as one of the axioms.

In this study we define a new observability measure for stochastic systems: the mutual information between the state sequence and the corresponding measurement sequence for a given time horizon.

We solve this model analytically for various measures of stochastic transcription and compare alternative gene-regulation designs.

This provides a measure of stochastic extinction risk in a range of persistent populations.

In this paper, an implicit compensated Euler method is introduced for stochastic differential equations with Poisson random measure.

By characterizing the worst-case measure for a related stochastic minimax game, it is shown that the worst-case uncertain system can be represented in the form of a parametric perturbation of the nominal system.

Pei et al. [46] considered the averaging principle for stochastic hyperbolic- parabolic equations driven by Poisson random measures with slow and fast time-scales.

Budhiraja et al. [14] studied the moderate deviation principles for stochastic differential equations driven by a Poisson random measure in finite and infinite dimensions.

In Section 2 we define the fundamental concepts and notations for stochastic integration with respect to Wiener processes and Lévy random measures.

Figure 9 QQ plots for stochastic regression.