A region contains n prey labeled 1, 2, …, n. Prey i is captured at the random time Zi; where Z1, Z2, …, Zn are i.i.d. with distribution function F. The statistician must decide when to stop searching, with the goal of maximizing the number of prey captured minus a linear time cost, c. The optimal strategy and its expected payoff are studied asymptotically as n, c →∞, for F a beta or Weibull distribution.

This paper formulates the random process of line-segments in the Euclidean plane. Under conditions more general than Poisson, expressions are obtained, for Borel A ⊂ R2, for the first moments of M(A), the number of segment mid-points in A; N(A), the number of segments which intersect with convex A; S(A), the total length within A of segments crossing A; and C(A) the number of segment-segment crossings within A. In the case of Poisson mid-points, the distribution of the rth nearest line-segment to a given point is found.

Let {Xn} be an infinite sequence of independent non-negative random variables. Let the distribution function of Xi, i = 1, 2, …, be either F1 or F2 where F1 and F2 are distinct. Set Sn = X1 + X2 + … + Xn and for t > 0 define and Zt = SN(t)+1 – t. The limit distributions of N(t), Yt and Zt as t → ∞ are obtained when F1 and F2 are in the domains of attraction of stable laws with exponents α1 and α2, respectively and Sn properly normalised has the composition of these two stable laws as its limit distribution.

We study record times, mainly, and sizes in the following context. Let Xn denote the size of the nth event occurring in a point stochastic pacing process 𝒫. The Xn is i.i.d. and 𝒫 is, variously, Poisson, negative binomial, renewal and Furry. Explicit distributions of first-record times are found, domains of attraction studied and the asymptotic lognormality of the nth-record time is shown for Poisson 𝒫.

A discrete-time, scalar, pursuit-evasion game is considered in which an evader, moving according to a stationary stochastic process, is continually being tracked by a pursuer. The pursuer's observations of the evader's positions are subject to observation error and time lag. It is shown that when the pursuer's strategy is linear in his information, the evader is able to infer the values of the observation errors (subject to the same time lag) and it is assumed that he utilises this information linearly in his evasion strategy. The payoff is taken to be the mean-square distance between pursuer and evader, the latter being subject to a quadratic constraint on his movement. The upper value and the corresponding strategies are found for all values of the variance θ of the observation error. The lower value problem is solved completely only for variances θ below a certain critical value and partially for variances above this critical value.

A well-known maintenance procedure for stochastically failing units is the age replacement policy (ARP). Under the ARP a unit is replaced on failure, or when it reaches a predetermined critical age.

In previous papers concerned with this procedure the authors searched for the optimal critical age to minimize the objective function under discussion. The purpose of this paper is to prove that the ARP itself is the optimal decision rule amongst all reasonable policies.

We consider a two-dimensional stochastic process of binary variables xij defined so that the conditional distribution of xij given its predecessors depends only on xi–1j and xij–1 Properties of the equilibrium distribution, when this exists, are investigated using three different representations of the process and explicit results are given in some special cases.

Consider a system that is subject to a sequence of randomly occurring shocks; each shock causes some damage of random magnitude to the system. Any of the shocks might cause the system to fail, and the probability of such a failure is a function of the sum of the magnitudes of damage caused from all previous shocks.

The purpose of this paper is to derive the optimal replacement rule for such a system whose cumulative damage process is a semi-Markov process. This allows for both the time between shocks and the damage due to the next shock to be dependent on the present cumulative damage level.

Only policies within the class of control-limit policies will be considered; namely, policies with which no action is taken if the damage is below a fixed level, and a replacement is made if the damage is above that.

An example will be given illustrating the use of the optimal replacement rule.

This paper presents a modification to the traditional technique of approximating general queueing problems by the Erlang distributions. It presents both theory and the results of some numerical testing.

This paper considers the distribution of distance between random points and shows how the distribution can be found when the points are chosen uniformly and independently in a hypersphere or in two adjacent unit squares. The value of a powerful extension of the classical Crofton technique is illustrated here for solving such geometric probability problems. This method is quite different from those employed by Hammersley and Oser.

In the theory of dams with Markovian inputs explicit results are not usually obtained, as the theory depends very heavily on the largest eigenvalue of the matrix (pijzj) where pij are the transition probabilities of the input process. In this paper we show that explicit results can be obtained if one considers an input process of a special form. The probability distribution of the time to first emptiness is obtained for both the finite and the infinite dam, as well as the stationary distribution of the dam content for the finite dam. Explicit results are given for the case where the stationary distribution of the input process has a geometric distribution.

The problem of detecting the arrival of a ‘disorder' in a process observed through a monitoring facility which may operate in ‘slow’ or ‘fast’ mode, is formulated as an optimal stopping problem. It is shown that in all circumstances where there may exist an optimal policy specifying the mode of observation and the time of stopping there is a unique policy satisfying certain necessary conditions of optimality; the various circumstances and control policies are specified.

Epidemic models have generally assumed that the population mixes homogeneously. More realistic models can be obtained by dividing the population into sub-populations within which mixing is homogeneous but between which mixing is heterogeneous. We consider such a model for the general epidemic process and present a formulation which permits inroads to be made towards obtaining explicit solutions to the state probabilities.

Trajstman (1974) has shown that two different population models used to study the number of mutant forms maintained in a population have a certain marginal stationary distribution in common. In this note a general stochastic population model is proposed which subsumes these two models and shows that their transition rates are also related.

A supercritical position-dependent Markov branching process has been used as an approximation to a model describing the initial geographical spread of a measles epidemic (Bartlett (1956)). Let α be its Malthusian parameter, ß its velocity of propagation, Z(A, t) the number of individuals in the set A at time t, and A√(ßt) = [√(ßt) r: r ∈ A]. The mean square convergence of the random variable W(A, t)= e–αtZ(A√(ßt), t) to a limit variable W(A) is established.

Messages arrive at a group of service channels in accordance with a time-dependent Poisson process. An arrival either (i) immediately begins k -stage Markovian service if an empty channel is reached, or (ii) balks and enters a retrial population if the channel sought is busy. Diffusion approximations to the number of messages in service (each stage) and in the retrial population are derived by writing stochastic differential equations (I + 0). Steady-state distributions are found and compared with certain simulation results.

In the paper the problem of the extinction of non-homogeneous Galton-Watson processes with one type of particle is studied. It is proved that the process becomes extinct if the expectations of the process do not converge to a limit (finite or infinite). If the expectations have a finite limit, then simple necessary and sufficient conditions for the extinction are proved. The general case remains open; however two more sufficient conditions which are also necessary under some restrictions are given.

This paper considers the expression of expectations and density function of the extinction time for a continuous-time Markov branching process.

Two independent i.i.d. sequences of random variables {Un} and {Dn} generate a Markov process {Xn} by Xn = max(Xn–1 – Dn, Un), n = 1, 2, …. ‘Exchange’ is defined as the event [Un > Xn–1 – Dn]. Conditions for existence of a limiting distribution for {Xn} are established, and normalization is discussed when no limiting distribution exists. Finally the process {Xn at the k th exchange; k = l, 2, …} and the time between consecutive exchanges are considered.

The limiting behavior of the probability of extinction of critical generation-dependent branching processes is determined. Lower bounds are determined for this probability. A means of classifying these branching processes is suggested.