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Therefore, D n (t) should be a discrete random variable.
Let be a discrete random variable with a probability density function.
Let F be a discrete random variable with n states, (f_i (i = 1ldots n)), where (f_i) denotes a data feature, and C be a discrete random variable with m value, (c_j (j = 1ldots m)), where (c_j) represents a classification type.
On the other hand, let K n be a discrete random variable obeying the uniform distribution on {1,2,…,n}.
If X 1 and X 2 are independent of each other, their functional operation result will be a discrete random variable which can be obtained through the operation of UGF.
Further, let N be a discrete random variable following a power series (PS) distribution (truncated at zero) with pmf p_{n} = P(N=n)=frac{a_{n}, theta^{n}}{C theta)}, n=1,2,ldots, (9).
Similar(51)
Therefore we may assume that N is a discrete random variable.
Chalone concentration C is a discrete random variable, which can be described by a probability mass function f(c).
A discrete Markov process is a discrete random process with the property that the next state depends only on the current state.
Different from DT, the total consumed power for CT to transmit one packet is a discrete random variable and can be statistically described as follows, (10).
For the non-homogenous Poisson arrivals, due to a technicality, we assume that the service time is a discrete random variable.
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