Approximate Bayesian computation with path signatures

Approximate Bayesian computation with path signatures

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...

书目详细资料
Main Authors:	Dyer, J, Cannon, P, Schmon, SM
格式:	Conference item
语言:	English
出版:	Proceedings of Machine Learning Research 2024

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