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The phrase "a joint probability mass" is correct and usable in written English.
It can be used in contexts related to statistics, probability theory, or data analysis when discussing the probability distribution of two or more random variables.
Example: "To analyze the relationship between the two variables, we need to calculate a joint probability mass for their possible outcomes."
Alternatives: "a joint probability distribution" or "a combined probability mass".
Exact(1)
MI I(x; y) is represented as: p x, y) is a joint probability mass function, and p(x) and p y) are marginal probability mass functions.
Similar(59)
Let P x,y ) represents a genotypic joint probability mass function, whose entries are the probability of genotype combinations from both SNPs.
A common approach to modeling such networks is by a master equation that governs the dynamic evolution of the joint probability mass function of the underlying population process and naturally leads to Markovian dynamics for such process.
Let us assume that an MP will experience i times of collision and j empty slot times before successfully reserving an MDAOP in CP with n MPs to contend, whose joint probability mass function, Pss i, j, n), can be obtained by (8), where I and J are the random variables that an MP will experience i times of collision and j empty slots before successfully reserving an MDAOP in CP, respectively.
An extension that considers the joint probability mass functions of link travel times varying with time is discussed.
Randomness of genotypes was accounted for by deriving the joint probability mass function of marker genotypes conditional on allelic frequencies and pedigree information.
This paper investigates the constrained shortest path problem in a transportation network where the link travel times are assumed to be random variables defined on the basis of joint probability mass functions.
Joint probability mass distribution p X, Y).
Let denote the joint probability mass function of the sources, which take values in discrete alphabets.
Pss i, j, n) The joint probability mass function of the random variables I and J with n MPs.
Explicitly, for the histogram matrix Q of bin occupancy counts, we estimate the joint probability mass function p by normalising p ̂ X, Y ( x, y ) = Q xy ∑ i ∑ j Q ij (15).
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