Sentence examples for deviation of the stochastic from inspiring English sources

Dotted lines indicate irrigation coefficient profiles calculated using ±1 standard deviation of the stochastic model burrow densities and surface areas.

The standard deviation of the stochastic annual component (se) is 0.526 for pine, 0.990 for spruce, 1.027 for birch and 0.938 for hardwood other than birch.

Next, taking into account that the expectation value represents the average level and the variance indicates the deviation of the stochastic variable, the stochastic objective function in (2.1) is transformed into (2.2).

To check the real performance of the structure, the material properties, the geometrical and execution variables that influence the selected degradation models have to be checked by quality control in order to define and to update the statistical quantities (type of distribution, mean value, standard deviation) of the stochastic variables that affect structural reliability.

Thus, (E{ | delta y_{k} |_{1}}) can be understood as the expectation of the stochastic variable (| delta y_{k} |_{1}) or the first-order deviation of the stochastic output (y_{k}(t)) ((t in S)) with respect to the desired output (y_{d}(t)) ((t in S)).

The test statistic t = d ^ i s u i i a t-distribution with degree of freedom (M - 1) - (N + 2) is used to assign a p-value for rejecting the null hypothesis H 0 : d i = 0, where u ii is the ith diagonal element of the matrix (Φ T Φ -1 and s = (Y − Φ -1θ ^ ) T (Y − Φ ⋅ θ ^ ) (M − 1 ) − (N + 2 ) is andunbiased estimator of σ (the standard deviation of the stochastic noise ε[ t]) [ 24].

For all considered prediction scenarios the standard-deviation of the stochastic estimate is consistent with the measurement ground truth.

(iv) Metabolic rate δ, size-specific consumption coefficient α, size-specific consumption exponent γ, and standard deviation σ of the stochastic component of the growth equation.

In [23], Benson et al. stated that solutes moving through a highly heterogeneous aquifer violate the basic assumptions of local second-order theories because of large deviations from the stochastic process of Brownian motion.

The Taylor-expansion approximation shared the problems of moment closure at small numbers of initial infected individuals, but was less stable with typically larger deviations from the mean of the stochastic model.

We use a large deviation principle and the stochastic Taylor expansion with respect to the topology of the space of geometric rough paths.