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However, the probability distributions of many random variables are often truncated in engineering applications.
Random variables considered are composite girder, barrier, and sidewalk flexural strengths (each in turn composed of many random variables), load magnitude (dead load and truck traffic live load), and live load position.
This involves two significant difficulties: assessing current information, which can be hard when it involves many random variables and/or many states, and deciding how this information can be used in the design of individual decision rules.
But if you have several independent random variables with the exact same distribution, if the number is super large let's say 100 million and you plot how many random variables fall into each point into a graph, you'll know that it has to look very close to this curve.
As per central limit theory, under normal conditions, the sum of many random variables will have an approximately Gaussian distribution.
J r (i) is the sum of many random variables; each of which represents an interference term caused by PU transmission over different subcarriers.
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Hence Corollaries 3.8 and 3.9 hold for many dependent random variables as well as independent random variables.
Energy harvesting rate is Gamma-distributed in stochastic case, because Gamma distribution can model many positive random variables [26] and remain unchanged in each timeslot.
But perhaps one of the main practical uses of the normal distribution is to model empirical distributions of many different random variables encountered in practice.
Sparse Bayesian factor analysis (sBFA) is a statistical method for modeling many dependent random variables through linear combinations of a few hidden variables (Gorsuch, 1983).
Further, pairwise measures do not characterize association well if a given genetic system involves many correlated random variables, as in multi-locus measures of LD.
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