SIAM/ASA Journal on Uncertainty Quantification, Volume 9, Issue 2, Page 788-817, January 2021.
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 [T. P. Prescott and R. E. Baker, SIAM/ASA J. Uncertain. Quantif., 8 (2020), pp. 114--138]. Previous work has considered MF-ABC only in the context of rejection sampling, which does not explore parameter space particularly efficiently. In this work, we integrate the multifidelity approach with the ABC sequential Monte Carlo (ABC-SMC) algorithm into a new MF-ABC-SMC algorithm. We show that the improvements generated by each of ABC-SMC and MF-ABC to the efficiency of generating Monte Carlo samples and estimates from the ABC posterior are amplified when the two techniques are used together.

中文翻译：

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具有顺序蒙特卡罗参数采样的多保真近似贝叶斯计算

SIAM/ASA 不确定性量化杂志，第 9 卷，第 2 期，第 788-817 页，2021 年 1 月。
多保真近似贝叶斯计算 (MF-ABC) 是一种用于参数推断的无似然技术，它利用模型近似来显着提高 ABC 算法的速度 [TP Prescott 和 RE Baker, SIAM/ASA J. Uncertain。Quantif., 8 (2020), pp. 114--138]。以前的工作仅在拒绝采样的背景下考虑了 MF-ABC，这并没有特别有效地探索参数空间。在这项工作中，我们将多保真方法与 ABC 顺序蒙特卡罗 (ABC-SMC) 算法集成到新的 MF-ABC-SMC 算法中。我们表明，当这两种技术一起使用时，ABC-SMC 和 MF-ABC 对生成蒙特卡罗样本的效率和 ABC 后验估计的效率的改进会被放大。