no code implementations • 21 Feb 2020 • Owen Thomas, Raquel Sá-Leão, Hermínia de Lencastre, Samuel Kaski, Jukka Corander, Henri Pesonen
To advance the possibilities for performing likelihood-free inference in higher dimensional parameter spaces, we introduce an extension of the popular Bayesian optimisation based approach to approximate discrepancy functions in a probabilistic manner which lends itself to an efficient exploration of the parameter space.