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Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation

Dyer, Joel and Cannon, Patrick and Schmon, Sebastian M. (2022) 'Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation.', Artificial Intelligence and Statistics 2022 (AISTATS): The 25th International Conference on Artificial Intelligence and Statistics Virtual, 28-30 March 2022.

Abstract

Simulation models of complex dynamics in the natural and social sciences commonly lack a tractable likelihood function, rendering traditional likelihood-based statistical inference impossible. Recent advances in machine learning have introduced novel algorithms for estimating otherwise intractable likelihood functions using a likelihood ratio trick based on binary classifiers. Consequently, efficient likelihood approximations can be obtained whenever good probabilistic classifiers can be constructed. We propose a kernel classifier for sequential data using path signatures based on the recently introduced signature kernel. We demonstrate that the representative power of signatures yields a highly performant classifier, even in the crucially important case where sample numbers are low. In such scenarios, our approach can outperform sophisticated neural networks for common posterior inference tasks.

Item Type:Conference item (Paper)
Full text:(AM) Accepted Manuscript
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Status:Peer-reviewed
Publisher Web site:http://proceedings.mlr.press/v151/
Date accepted:18 January 2022
Date deposited:24 June 2022
Date of first online publication:03 May 2022
Date first made open access:24 June 2022

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