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...
| 主要な著者: | , |
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| フォーマット: | Journal article |
| 言語: | English |
| 出版事項: |
Society for Industrial and Applied Mathematics
2021
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