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Joint modeling of multiple health related random variables is essential to develop an understanding for the public health consequences of an aging population.
Suprathermal X-ray emission, generation of energetic ions, x-ray trapping due to multiple scattering and the x-my bursts related ("random" laser) are illustrated.
In this paper, we propose Markov random field models for pattern recognition, which provide a flexible and natural framework for modelling the interactions between spatially related random variables in their neighbourhood systems.
In fact, stability number and damage level are closely related random variables and their relationship can best be modeled by methods that explicitly take uncertainty into account as vagueness, ambiguities, and imprecision.
This idea was later extended into a complex model that related random statistical rotations of suspension cells to EP efficiency [10].
It was unclear if these observations were causally related, random occurrences, or detection bias.
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At the same time, the sums of matricially free random variables, called random pseudomatrices, are closely related to random matrices.
We give some basic concepts related to random attractors and random invariant manifolds.
However, we should note that corresponding parameter estimates may be severely affected if some of the adopted covariates/factors, for example, in this case increased inflammatory markers, are endogenous, that is, if they are related to random effects and/or random errors in the equation for the presence of fatigue symptoms.
In Section 2, we recall some basic concepts and already known results related to random dynamical systems and random attractors.
Some results related to random sums of independent integer-valued random variables are also investigated.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com