We consider a population of n projects which in general continue to evolve whether in operation or not (although by different rules). It is desired to choose the projects in operation at each instant of time so as to maximise the expected rate of reward, under a constraint upon the expected number of projects in operation. The Lagrange multiplier associated with this constraint defines an index which reduces to the Gittins index when projects not being operated are static. If one is constrained to operate m projects exactly then arguments are advanced to support the conjecture that, for m and n large in constant ratio, the policy of operating the m projects of largest current index is nearly optimal. The index is evaluated for some particular projects.

A method is derived for choosing the width of a spectral window in spectrum estimation. The method is based upon the idea of minimising complexity, i.e. the length, in bits, of a code for the data.

In this paper we trace the development of the asymptotic analysis of autocorrelations for stationary purely non-deterministic time series. We emphasize the interplay between mathematical requirements and modelling philosophy. We then proceed to extend the theory to the case where only a certain weak form of asymptotic independence of the linear prediction errors is needed rather than the earlier martingale difference or independence requirements.

Present developments in computer performance evaluation require detailed analysis of N-dimensional random walks on the set of lattice points in the first 2N-ant of Recent research has shown that for the two-dimensional case the inherent mathematical problem can often be formulated as a boundary value problem of the Riemann–Hilbert type. The paper is concerned with a derivation and analysis of an identity for the first entrance times distributions into the boundary of such random walks. The identity formulates a relation between these distributions and the zero-tuples of the kernel of the random walk; the kernel contains all the information concerning the structure of the random walk in the interior of its stage space. For the two-dimensional case the identity is resolved and explicit expressions for the entrance times distributions are obtained.

This paper considers a bivariate random walk model on a rectangular lattice for a particle injected into a fluid flowing in a tank. The numbers of jumps of the particle in the x and y directions in this particular model are correlated. It is shown that when the random walk forms a bivariate Markov chain in continuous time, it is possible to obtain the state probabilities pxy(t) through their Laplace transforms. Two exit rules are considered and results for both of them derived.

The hypercubic lattice with bonds of random electrical resistance affords a model for the specific conductivity of microscopically irregular material. The resulting stochastic process lies between a subadditive and a superadditive process: mesoadditive processes of this type provide several unsolved problems for pure mathematicians.

Let Γn(p) denote a random graph with n vertices in which any two vertices, independently of the others, are connected by an edge with probability p where . Denote by γ n(p) the total number of cycles in the graph Γn(p). The main object of this paper is to prove that the limit distribution of γ n(λ /n) is a Poisson distribution with expectation if n → ∞and 0 < λ< 1.