In this graph, nodes represent the random variables and directed edges represent stochastic dependencies among variables.
The shaded nodes represent the random variables we observe, namely, the alleles observed at each locus.
We consider statistical significance testing for the mutual information measurement M (X, Y), where X and Y represent the random variables associated to the considered two gene expressions.
The first component, G, is a directed acyclic graph (DAG) whose vertices represent the random variables u1, u2,…, u N and whose edges correspond to dependencies between variables.
The notation X sometimes represents the random variable with PDF f and sometimes represents the random variable with PDF g.
Indeed, if represents the random variable modelling an -bit Hamming distance sequence, then (25).
ζ k i represents the random variable such that it would be one when an outage occurs and zero when there is no outage.
The RUL can be expressed as (X_{t} = T - t|T > t,varvec{Z}(t)), where T represents the random variable of lifetime, t the current time and vector Z t) the observations of the covariate variables available up to the current time t.
The Pearson correlation coefficients are computed using Eq. (15) where Xk represents the random variable corresponding to the k-th component of the input instance vectors (k = 1,2,) and Y is the random variable representing the class labels.
The probability to obtain an intersection size | I t| is computed by the formula, where x represents the random variable for the intersection of two target sets.
