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.
no code implementations • 12 Dec 2019 • Owen Thomas, Jukka Corander
Here we show how probabilistic classifiers can be employed to resolve this issue.
1 code implementation • 30 Nov 2016 • Owen Thomas, Ritabrata Dutta, Jukka Corander, Samuel Kaski, Michael U. Gutmann
The popular synthetic likelihood approach infers the parameters by modelling summary statistics of the data by a Gaussian probability distribution.