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...
| Autors principals: | , , |
|---|---|
| Format: | Conference item |
| Idioma: | English |
| Publicat: |
Proceedings of Machine Learning Research
2024
|