Abstract: Approximate Bayesian computation (ABC) is a method for calculating a posterior distribution when the likelihood is intractable, but simulating the model is feasible. It has numerous important applications, for example in computational biology, material physics, user interface design, etc. However, many ABC algorithms require a large number of simulations, which can be costly. To reduce the cost, Bayesian optimisation (BO) and surrogate models such as Gaussian processes have been proposed. Bayesian optimisation enables deciding intelligently where to simulate the model next, but standard BO approaches are designed for optimisation and not for ABC. Here we address this gap in the existing methods. We model the uncertainty in the ABC posterior density which is due to a limited number of simulations available, and define a loss function that measures this uncertainty. We then propose to select the next model simulation to minimise the expected loss. Experiments show the proposed method is often more accurate than the existing alternatives.

Speaker: Pekka Marttinen

Affiliation: Academy Research Fellow, Department of Computer Science, Aalto University

Place of Seminar: Aalto University