Costs and decisions in population management: Koalas on Kangaroo Island

Many populations have a negative impact on their habitat, or upon other species in the environment, if their numbers become too large. For this reason they are often managed using some form of control. The objective is to keep numbers at a sustainable level, while ensuring survival of the population.+Here we present models that allow population management programs to be assessed. Two common control regimes will be considered: reduction and suppression. Under the suppression regime the previous population is maintained close to a particular threshold through near continuous control, while under the reduction regime, control begins once the previous population reaches a certain threshold and continues until it falls below a lower pre-defined level. We discuss how to best choose the control parameters, and we provide tools that allow population managers to select reduction levels and control rates. Additional tools will be provided to assess the effect of different control regimes, in terms of population persistence and cost.In particular we consider the effects of each regime on the probability of extinction and the expected time to extinction, and compare the control methods in terms of the expected total cost of each regime over the life of the population. The usefulness of our results will be illustrated with reference to the control of a koala population inhabiting Kangaroo Island, Australia.

A partial-correctness semantics for modelling assembler programs

Previous work on formally modelling and analysing program compilation has shown the need for a simple and expressive semantics for assembler level programs. Assembler programs contain unstructured jumps and previous formalisms have modelled these by using continuations, or by embedding the program in an explicit emulator. We propose a simpler approach, which uses techniques from compiler theory in a formal setting. This approach is based on an interpretation of programs as collections of program paths, each of which has a weakest liberal precondition semantics. We then demonstrate, by example, how we can use this formalism to justify the compilation of block-structured high-level language programs into assembler.

Prediction with Gaussian processes: from linear regression to linear prediction and beyond

The main aim of this paper is to provide a tutorial on regression with Gaussian processes. We start from Bayesian linear regression, and show how by a change of viewpoint one can see this method as a Gaussian process predictor based on priors over functions, rather than on priors over parameters. This leads in to a more general discussion of Gaussian processes in section 4. Section 5 deals with further issues, including hierarchical modelling and the setting of the parameters that control the Gaussian process, the covariance functions for neural network models and the use of Gaussian processes in classification problems.

Towards a framework for combining stochastic and deterministic descriptions of nonstationary financial time series

We present in this paper ideas to tackle the problem of analysing and forecasting nonstationary time series within the financial domain. Accepting the stochastic nature of the underlying data generator we assume that the evolution of the generator's parameters is restricted on a deterministic manifold. Therefore we propose methods for determining the characteristics of the time-localised distribution. Starting with the assumption of a static normal distribution we refine this hypothesis according to the empirical results obtained with the methods anc conclude with the indication of a dynamic non-Gaussian behaviour with varying dependency for the time series under consideration.