This paper considers the autocorrelation function of queue length and the corresponding spectral density (i.e., its Fourier transform). Some general expressions are obtained using generating functions and matrices, and applied to M/M/1 and M[x]/M/∞ queues.

Probability properties of the measure of the union of random sets have theoretical as well as practical importance (David (1950), Garwood (1947), Hemmer (1959)). In the present paper we derive asymptotic properties of the distributions of these measures and apply the derived properties to the investigation of the asymptotic behavior of empirical distribution functions. Thus, an asymptotic distribution function for the relative lengths of steps in the empirical distribution function is obtained.

The asymptotic distribution of the traffic average in the case of M/G/∞ with bulk arrivals has been proved in [7] to be normal with mean μCμPμF and variance where and are the variances and μP and μc are the means of the service-time and the batch-size respectively, μF being the mean inter-arrival time. We prove in this note that the asymptotic normality holds even in the more general case of a G//G/∞ with bulk arrivals.

In the paper [1], Hájek and Rényi established an inequality which they formulated in the following way: X1,X2,··· are independent random variables and . For each k, EXk = 0 and , while is a non-increasing sequence of positive numbers. Then, for any ε > 0 and any positive integers n and m (n < m), The well-known Kolmogorov inequality is the particular case ck = 1, all k, and n = 1 of (1). It is the object of the present note to produce an extended version of (1) where no moment conditions need be satisfied. This provides a useful general bound and illuminates the role of certain standard techniques in the study of the almost sure behaviour of sums of independent random variables.

In the study of dynamical systems perturbed by noise, it is important to know whether the stochastic process of interest has a stationary distribution. Four necessary and sufficient conditions are formulated for the existence of a finite invariant measure for a Feller process on a σ-compact metric (state) space. These conditions link together stability notions from several fields. The first uses a Lyapunov function reminiscent of Lagrange stability in differential equations; the second depends on Prokhorov's condition for sequential compactness of measures; the third is a recurrence condition on the ergodic averages of the transition operator; and the fourth is analogous to a condition of Ulam and Oxtoby for the nonstochastic case.

Let Xi, i = 1, 2, 3,… be a sequence of independent and identically distributed random variables with law ℓ(X) and write. if EX= 0 and EX2=σ2< ∞, the law of the iterated logarithm (Hartman and Wintner [1]) tells us that

Let F(z) = σ fjzj be the generating function for the offspring distribution {fj} from a single ancestor in the usual Galton-Watson process. It is well-known (see Harris [1]) that if Π(z) is the generating function of the distribution of ancestors in the 0th generation, the distribution of offspring at the nth generation has generating function where Fn(z), the nth functional iterate of F(z), gives the distribution of offspring at the nth generation from a single ancestor.

Let {X(n): n = 0, 1, 2, …} be a Markov chain with state space {0, 1, 2, …, N} and transition probability matrix P = (pij).

In the M/G/1 queue, the joint distribution of the virtual waitingtime and the residual duration of the busy period at any time t, is given by a formula analogous to the one for the virtual waitingtime by itself. In the limit, as t → ∞, the limiting joint distribution is given by a formula of the Pollaczek-Khintchine type.