In the present exposition we are concerned only with the simple Galton-Watson process, initiated by a single ancestor (Harris (1963), Chapter I). Let denote the probability generating function of the offspring distribution of a single individual. Our fundamental assumption, which holds throughout the sequel, is that fj ≠ 1, j = 0,1,2, …; in particular circumstances it shall be necessary to strengthen this to 0 < f0 ≡ F(0) < 1, which is the relevant assumption when extinction behaviour is to be considered. (Even so, our assumptions will always differ slightly from those of Harris (1963), p. 5.)

Consider a stochastic process X(t) (t ≧ 0) taking values in a countable state space, say, {1, 2,3, …}. To be picturesque we think of X(t) as the state which a particle is in at epoch t. Suppose the particle moves from state to state in such a way that the successive states visited form a Markov chain, and that the particle stays in a given state a random amount of time depending on the state it is in as well as on the state to be visited next. Below is a possible realization of such a process.

Suppose that, e.g., in a guessing experiment, the chance of a ‘hit’ at each ‘attempt’ is the constant p, 0 < p < 1. Let q = 1 - p. In a large number n of attempts the ‘average’ number of hits is pn. Let m = (1 + k)pn.

Recently Kshirsagar and Gupta [5] obtained expressions for the asymptotic values of the first two moments of a Markov renewal process. The method they employed involved formal inversion of matrices of Laplace-Stieltjes transforms. Their method also required the imposition of a non-singularity condition. In this paper we derive the asymptotic values using known renewal theoretic results. This method of approach utilises the fundamental matrix of the imbedded ergodic Markov chain and the theory of generalised matrix inverses. Although our results differ in form from those obtained by Kshirsagar and Gupta [5] we show that they reduce to their results under the added non-singularity condition. As a by-product of the derivation we find explicit expressions for the moments of the first passage time distributions in the associated semi-Markov process, generalising the results of Kemeny and Snell [4] obtained for Markov chains.

This is the first part of a three part work, the common setting being Ed, Euclidean space of d dimensions. Many random physical phenomena, often in the form of structures, admit models which are assemblages of random s-flats in Ed. Indeed, taking s = 0, any n-sample from a d-dimensional distribution may of course be so regarded! For static phenomena generally 0 ≦ s < d ≦ 3, while 4 is to be substituted for 3 if time variation is allowed. By postulating a high degree of stochastic independence and uniformity, a variety of “simple” models is defined. However, their investigation poses problems in geometrical probability of a wide range of difficulty; the solutions of many of which are still far from complete.

Many problems in geometrical probability have been surveyed in the monograph by Professor M. G. Kendall and myself (Kendall and Moran (1963)). In 1966 I published a note in J. Appl. Frob. 3 describing researches which had either been carried out since that book appeared, or which had escaped our notice. The present paper attempts to describe still further investigations classified roughly as to whether they are concerned with points, lines, planes, estimation of area and length, and coverage.

The theory of storage processes, originally formulated by Moran [1] in 1954, has developed in the past fourteen years into a minor subfield of Applied Probability, closely allied to queueing theory. While dam models with discrete inputs are analogous to queueing processes, the essentially continuous nature of water inflows has distinguished generalized storage processes from queues. Indeed, some of the most complex of storage problems have arisen in the case of continuous flows.

The problem of allocating scientific effort between a number of competing projects is considered. A stochastic mathematical model is set up, and for the case of just two competing projects a number of results are proved which enable the best policy to be determined. An indication is given of the ways in which these results might be extended so as to deal with more general situations.

We consider sequential decision processes in which the posterior distribution of some unobservable must be included among the sufficient statistics. The technique of an earlier paper (Whittle (1964)) is applied to show that the residual loss function is necessarily homogeneous of degree one in this distribution, if certain conventions are adopted. This point leads to considerable simplification. Examples are given of the great variety of problems which can advantageously be formulated in this manner.

Many problems arising in the physical and social sciences relate to processes which happen sequentially. Such processes are usually investigated by means of the theory of stationary stochastic processes, but there have been some attempts to develop techniques which are not subject to the conceptual difficulties inherent in the probabilistic approach. These difficulties stem from the fact that in practice one is often restricted to a single record which, from the probabilistic point of view, is only one sample from an ensemble of possible records. In some instances such a viewpoint seems artificial, and for some time series it is questionable whether any objective reality corresponds to the idea of an ensemble of possible time series. For example, as noted in Feller (1967), a theory of probability based on a frequency interpretation cannot meaningfully attach a probability to a statement such as “the sun will rise tomorrow”, because to do so one would have to set up a conceptual universe of possible worlds.