Sentence examples for stochastic measure from inspiring English sources

Let V (ds) is basic cross stochastic measure, namely V ( Δ 1 ) = ∫ - ∞ ∞ e i λ t 2 - e i λ t 1 i λ Λ ( d λ ).

Sill variance, a stochastic measure of distribution of aBMD, had significant relationships with microarchitecture parameters of trabecular bone, including bone volume fraction, bone surface-to-volume ratio, trabecular thickness, trabecular number, trabecular separation and anisotropy.

The temperature array as a result of measurements contains a stochastic measuring tolerance.

Approaches that exist in the literature to quantify the flexibility for a given design involve the deterministic measures, such as the resilience index (RI), the flexibility index, and the stochastic measures––such as the design reliability.

Our results highlight the sensitivity of stochastic measures to cell division fate and quantify the limitations of using certain approximations (such as the fixed-population and mean-field assumptions) in evaluating fixation times.

An output-only modal analysis method was used to identify the modal properties of the buildings: the random decrement technique was applied to the stochastic measured response, and then the time-domain random decrement signature was used for modal analysis by the Ibrahim Time Domain method.

Based on this stochastic robustness measure, we design novel resource allocation techniques that work in immediate and batch modes, with the goal of maximizing the number of tasks that meet their individual deadlines.

In this study, we define a stochastic robustness measure to facilitate resource allocation decisions in a dynamic environment where tasks are subject to individual hard deadlines and each task requires some input data to start execution.

The contribution of LGEM can be briefly described as that the generalization error is less than or equal to the summation of three terms: training error, stochastic sensitivity measure (SSM), and a constant.

Mutual information (and its conditional variant), applied to stochastic processes, measure contemporaneous statistical dependencies between stochastic dynamical processes evolving in time; that is, given joint processes X t,Y t, the mutual information I(X t  : Y t )at a given time t might be read as: The amount of information about the present of X resolved by the present of Y.

We extend the well-known relation between Markov semigroups and processes with independent increments to the case of generalized group-valued semigroups indexed by some measurable space and stochastic multiplicative measures.