Multifidelity approximate Bayesian computation with sequential Monte Carlo parameter sampling
Multifidelity approximate Bayesian computation (MF-ABC) is a likelihood-free technique for parameter inference that exploits model approximations to significantly increase the speed of ABC algorithms (Prescott and Baker, 2020). Previous work has considered MF-ABC only in the context of rejection sam...
| Автори: | , |
|---|---|
| Формат: | Journal article |
| Мова: | English |
| Опубліковано: |
Society for Industrial and Applied Mathematics
2021
|