Sentence examples for random suppose from inspiring English sources

Imagine a group of birds searching for food at random, suppose there is one and only one piece of food in a particular area, and none of the birds know where the food is.

In order to do so, we carried out two parallel experiments in which both tasks were presented in a baseline condition as well as in association with three different concurrent tasks (i.e., articulatory suppression, spatial tapping, and random generation) supposed to tap the various components of working memory.

with (i), the waveform signal emitted by a source (or a wavefied ),   (ii), a random amplitude of the source,   (iii), a time propagation between source and sensor depending of (the direction-of-arrival (DOA) of source ),   (iv), a random noise supposed to be additive, temporally and spatially white, uncorrelated with the sources, nonpolarized and with a power spectral density given by.

., the waveform signal emitted by a source (or a wavefied ),, a random amplitude of the source,, a time propagation between source and sensor depending of (the direction-of-arrival (DOA) of source ),, a random noise supposed to be additive, temporally and spatially white, uncorrelated with the sources, nonpolarized and with a power spectral density given by.

Following this way, we have assumed unbiased estimation of both parameters for the noise model (1) and estimates modeled as k ^ = k + Δk, σ ^ si 2 = σ ^ si 2 + Δ σ ^ si 2, where Δk and Δ σ ^ si 2 are mutually independent zero mean random variables supposed to be Gaussian with standard deviations δ k and δ v, respectively.

As a result, Alzaatreh et al. ([2012b]) defined and studied the T-geometric family, which are the discrete analogues of the distribution of the random variable T. Suppose F x) denotes the CDF of any random variable X and r(t) denotes the PDF of a continuous random variable T with support [a, b].

Theorem B. Let be a -mixing sequence of identically distributed random variables,,, and suppose that for.

Let Y n denote some arbitrary continuous random variable and suppose that the probability density of Y n evaluated at y n is the function f n (y n ).

Luxe gave me a clear picture of what he looked like and told me his favorite soccer player was Cristiano Ronaldo, which was random but I suppose the idea behind that is that maybe it helps you feel safer about who you are handing your keys over to.

Let { X n ; n ≥ 1 } be a sequence of ND random variables which is stochastically dominated by a random variable X. Suppose that 0 < α, β < ∞, 0 < p < 2 and 1 / p = 1 / α + 1 / β.

random variables drawn from the distribution function (F cdot)) of the random variable X. Suppose that there exist constants (0 < rho< infty) and (-infty< tau< infty) and a monotone function (h cdot): [0, infty) rightarrow 0, infty)) with (lim_{x rightarrowinfty}h(x^{2})/h(x) = 1) such that mathbb{P}(X > x) simfrac{(log x)^{tau}h(x)}{x^{rho }} quadtextit{as } x rightarrowinfty.