Amortised likelihood-free inference for expensive time-series simulators with signatured ratio estimation
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 l...
| Glavni autori: | , , |
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| Format: | Conference item |
| Jezik: | English |
| Izdano: |
Journal of Machine Learning Research
2022
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