Inference and Optimal Design for Nearest-Neighbour Interaction Models

Abstract

We consider problems of Bayesian inference for a spatial epidemic on a graph, where the final state of the epidemic corresponds to bond percolation, and where only the set or number of finally infected sites is observed. We develop appropriate Markov chain Monte Carlo algorithms, demonstrating their effectiveness, and we study problems of optimal experimental design. In particular, we demonstrate that for lattice-based processes an experiment on a sparsified lattice can yield more information on model parameters than one conducted on a complete lattice. We also prove some probabilistic results about the behaviour of estimators associated with large infected clusters.