Sentence examples for i the random variable from inspiring English sources

Corollary 1: Based on Lemma 1, if T is a random variable with PDF f T (x), then (i) The random variable X = θ cot(πe − T ) follows a distribution in the T-Cauchy{exponential} family.

Lemma 1: Let T be a random variable with PDF f T (x), then (i) The random variable X = − θ cot πF Y (T)) follows the T-Cauchy{Y} distribution.

Furthermore, for the second relay node, conditional on D 1 ′ = i, the random variable D 2 ′ = D 2 t A 0 A 1 is also a binomial variable with parameters D 1 ′ = i and R 1 L, namely, D 2 ′ ∼ B D 1 ′ = i, R 1 L. (45).

Similarly, for node 2, conditioning on D0,1 t)|A0 = i, the random variable D0,2 t)|also alsatisfiesies the binomial distribution with probability of success r 1 L, namely D 0, 2 t | A 0 A 1 ∼ B D 0, 1 t | A 0 = i, r 1 L. (49).

The probability density function of V i, the random variable representing class i vehicle velocity, is assumed to be uniform [13, 26] in the interval (vmin,i,vmax,i), with μ v i representing the mean and σ v i representing the standard deviation.

In other words, if we denote by S i the random variable associated to the source s i t then we need to analyze the behavior of the mixture random variable X defined as: X = α 1 S 1 + α 2 S 2 + ⋯ + α n S n. (6).

For each i, the random variables x ij and ε j are assumed to be independent.

If the top and bottom students are insignificant, that is to say, the variance Var ξ I of the random variable ξ I is close to 0, according to Figure 1 and Figure 2 with formula (4.7), we may think that there is a real number k ∈ ( 2, ∞ ) such that ξ I ∼ N k.

Lemma 4 Let T wait (D i ) denote the random variable representing the waiting time of the data item D i and T ret (D i ) denote the retrieval time of the data item D i. Then we have T access D i = T wait D i + T ret D i (10).

If N i is the random variable counting the number of occurrences (overlapping or renewal) of a given pattern in X1... X i.

Thus, for all i and the random variable (appearing in (3)) is a sum of Nakagami-m random phase vectors.