Efficient Bayesian Experimental Design for Implicit Models

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Bayesian experimental design involves the optimal allocation of resources in an experiment, with the aim of optimising cost and performance. For implicit models, where the likelihood is intractable but sampling from the model is possible, this task is particularly difficult and therefore largely unexplored. This is mainly due to technical difficulties associated with approximating posterior distributions and utility functions. We devise a novel experimental design framework for implicit models that improves upon previous work in two ways. First, we use the mutual information between parameters and data as the utility function, which has previously not been feasible. We achieve this by utilising Likelihood-Free Inference by Ratio Estimation (LFIRE) to approximate posterior distributions, instead of the traditional approximate Bayesian computation or synthetic likelihood methods. Secondly, we use Bayesian optimisation in order to solve the optimal design problem, as opposed to the typically used grid search or sampling-based methods. We find that this increases efficiency and allows us to consider higher design dimensions.
Original languageEnglish
Title of host publicationProceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019)
Place of PublicationNaha, Okinawa, Japan
Number of pages10
Publication statusPublished - 25 Apr 2019
Event22nd International Conference on Artificial Intelligence and Statistics - Naha, Japan
Duration: 16 Apr 201918 Apr 2019

Publication series

NameProceedings of Machine Learning Research
ISSN (Electronic)2640-3498


Conference22nd International Conference on Artificial Intelligence and Statistics
Abbreviated titleAISTATS 2019
Internet address


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