Abstract

Co-authors: Matthew Moores (QUT, Australia), Christian Robert (U. Paris Dauphine, France)

Existing algorithms for approximate Bayesian computation (ABC) assume that it is feasible to simulate pseudo-data from the model at each iteration. However, the computational cost of these simulations can be prohibitive for high dimensional data. An important example is the Potts model, which is commonly used in image analysis. The dimension of the state vector in this model is equal to the size of the data, which can be millions of pixels. We introduce a preparatory computation step before model fitting to improve the scalability of ABC . The output of this precomputation can be reused across multiple datasets. We illustrate this method by estimating the smoothing parameter for satellite images, demonstrating that the pre-computation step can sufficiently reduce the average runtime required for model fitting to enable analysis in realistic, if not yet real, time.