论文标题
通过顺序蒙特卡洛参数采样
Multifidelity Approximate Bayesian Computation with Sequential Monte Carlo Parameter Sampling
论文作者
论文摘要
多重差近似贝叶斯计算（MF-ABC）是一种用于参数推断的可能性技术，可利用模型近似值显着提高ABC算法的速度（Prescott and Baker，Baker，2020）。以前的工作仅在拒绝抽样的背景下才考虑了MF-ABC，这并不特别有效地探索参数空间。在这项工作中，我们将多重方法与ABC顺序蒙特卡洛（ABC-SMC）算法集成到新的MF-ABC-SMC算法中。我们表明，当两种技术一起使用时，ABC-SMC和MF-ABC的每种改进对生成蒙特卡洛样品的效率和从ABC后部产生的效率都会放大。
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 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.
