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Additionally, both are modeled as white Gaussian processes whereas the sum of distributions provides a nearly perfect match with the measured noise from the mobile spectrum monitoring equipment.
Mixture source assignment utilises a weighted linear sum of distributions (left { P_{theta }, theta in mathbb {M} right }) with a prior distribution on the predictor index set (mathbb {M}) [20,23].
By the just proven Theorem 9.3 and Remark 5 after the statement of the theorem: begin{aligned} D(H ={varphi in L^2,|, nabla varphi in L^2, V^{1/2}varphi in L^2, -Delta varphi +Vvarphi in L^2} end{aligned} (9.10 where (-Delta varphi +Vvarphi ) is viewed as a sum of distributions.
More specifically, where For a fixed mixture parameter, ρ, the test statistic Q ρ follows a weighted sum of distributions under the null hypothesis of no association.
QSKAT follows a weighted sum of distributions under the null hypothesis, the significance of which can be determined analytically, without the need for permutations.
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The sum of distribution of p τ (m n)·n is finally fed into classifier for classification.
For minimum ionising particles a sum of binomial distributions describes the lateral charge distribution.
In this work, we present a novel method for approximating a normal distribution with a weighted sum of normal distributions.
Non-Gaussian distributions are represented as a sum of normal distributions.
A Gaussian mixture model is a model of a complex probability distribution described as a weighted sum of Gaussian distributions with means x i and variances.
The average gene frequency distribution is equal to the sum of two distributions (1), with parameters θ1 and θ2, see Additional file 1: Appendix S5.
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