AISTATS 2022poster10 citations
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation
Joel Dyer, Patrick W. Cannon, Sebastian M. Schmon
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
BibTeX
@InProceedings{pmlr-v151-dyer22a,
title = { Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation },
author = {Dyer, Joel and Cannon, Patrick W. and Schmon, Sebastian M.},
booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics},
pages = {11131--11144},
year = {2022},
editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel},
volume = {151},
series = {Proceedings of Machine Learning Research},
month = {28--30 Mar},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v151/dyer22a/dyer22a.pdf},
url = {https://proceedings.mlr.press/v151/dyer22a.html},
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