Likelihood-free inference for simulator-based statistical models has developed rapidly from its infancy to a useful tool for practitioners. However, models with more than a handful of parameters still generally remain a challenge for the Approximate Bayesian Computation (ABC) based inference. To advance the possibilities for performing likelihood-free inference in higher dimensional parameter spaces, we introduce an extension of the popular Bayesian optimisation based approach to approximate discrepancy functions in a probabilistic manner which lends itself to an efficient exploration of the parameter space. Our approach achieves computational scalability for higher dimensional parameter spaces by using separate acquisition functions and discrepancies for each parameter. The efficient additive acquisition structure is combined with exponentiated loss -likelihood to provide a misspecification-robust characterisation of the marginal posterior distribution for all model parameters. The method successfully performs computationally efficient inference in a 100-dimensional space on canonical examples and compares favourably to existing modularised ABC methods. We further illustrate the potential of this approach by fitting a bacterial transmission dynamics model to a real data set, which provides biologically coherent results on strain competition in a 30-dimensional parameter space.

翻译：以模拟器为基础的统计模型的无概率推论从最初阶段迅速发展成为对实践者有用的工具,但具有少数几个参数的模型通常仍对基于高维参数空间的阿普约巴伊西亚计算法(ABC)推论构成挑战。为了提高在高维参数空间进行无概率推论的可能性,我们推广了流行的巴伊西亚优化法,以概率化方式将差异功能相近,从而有利于有效探索参数空间。我们的方法通过对每个参数分别使用获取功能和差异,实现了较高维度参数空间的计算可扩缩性。高效的添加剂获取结构与推导式损(推算法)相结合,为所有模型参数的边缘后端分布提供不精确的-紫色特性。这种方法成功地在100维空间对罐体实例进行计算高效推论,并与现有的模块化ABC方法相比较。我们进一步展示了这一方法的潜力,将细菌传播动态模型与实际空间维度设定的30维参数相协调,从而提供生物学结果。</s>