Approximate Bayesian computation with path signatures

Conference item

Simulation models often lack tractable likelihood functions, making likelihood-free inference methods indispensable. Approximate Bayesian computation generates likelihood-free posterior samples by comparing simulated and observed data through some distance measure, but existing approaches are often poorly suited to time series simulators, for example due to an independent and identically distributed data assumption. In this paper, we propose to use path signatures in approximate Bayesian computation to handle the sequential nature of time series. We provide theoretical guarantees on the resultant posteriors and demonstrate competitive Bayesian parameter inference for simulators generating univariate, multivariate, and irregularly spaced sequences of non-iid data.

Authors: Dyer, J; Cannon, P; Schmon, SM

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Proceedings of Machine Learning Research
• Host title: Proceedings of the 40th Conference on Uncertainty in Artificial Intelligence
• Volume: 244
• Pages: 1207-1231
• Publication date: 2024-09-12
• Acceptance date: 2024-04-26
• Event title: 40th Conference on Uncertainty in Artificial Intelligence (UAI 2024)
• Event location: Barcelona, Spain
• Event website: https://www.auai.org/uai2024/
• Event start date: 2024-07-15
• Event end date: 2024-07-19
• ISSN: 2640-3498
• Language: English