Abstract

Stochastic kinetic models are discrete valued continuous time Markov processes and are often used to describe biological and ecological systems. In recent years there has been interest in the construction of Bayes optimal experimental designs for these models. Unfortunately standard methods such as that by Muller (1999) are computationally intensive even for relatively simple models. However progress can be made by using a sequence of Muller algorithms, where each one has an increasing power of the expected utility function as its marginal distribution. At each stage efficient proposals in the design dimension can be made using the results from the previous stages. In this talk we outline this algorithm, investigate some computational efficiency gains made using parallel computing and illustrate the results with an example.