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.
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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|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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