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...
| Päätekijät: | , , |
|---|---|
| Aineistotyyppi: | Conference item |
| Kieli: | English |
| Julkaistu: |
Proceedings of Machine Learning Research
2024
|