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A measure distribution function is a function, which is left continuous on, non-decreasing, and.
The Markov Model is used to measure distribution of the number of requests between two requests for content k.
A measure distribution function is a function μ : R → [0, 1] which is left continuous, non-decreasing on R, inft∈Rμ(t) = 0 and supt∈Rμ(t) = 1.
Later on, Voss expressed it based on the probabilities and using the first and second order moments of the measure distribution (2).
The image outcome measure distribution volume ratio (DVR) was estimated using a reference tissue-based graphical method for reversible ligands [20].
Typically, the X-ray emission in these systems is modeled with a collisional plasma model, sometimes with an emission measure distribution taken from a cooling flow model.
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All exercise testing measure distributions were examined for normality.
In the development of the measure, distributions were found to be skewed, a pattern adjusted for by applying a square root transformation.
A non-measure distribution function is a function, which is right continuous on, non-increasing, and.
A non-measure distribution function is a function ν : R → [0, 1] which is right continuous, non-increasing on R, inft∈Rν(t) = 0 and supt∈Rν(t) = 1.
We denote by B the family of all non-measure distribution functions, and by G a special element of B defined by G ( t ) = { 1, if t ≤ 0, 0, if t > 0. If X is a nonempty set, then ν : X → B is called a probabilistic non-measure on X and ν ( x ) is denoted by ν x.
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