Sampling-based inference techniques are central to modern cosmological data analysis; these methods, however, scale poorly with dimensionality and typically require approximate or intractable likelihoods. In this paper we describe how Truncated Marginal Neural Ratio Estimation (TMNRE) (a new approach in so-called simulation-based inference) naturally evades these issues, improving the $(i)$ efficiency, $(ii)$ scalability, and $(iii)$ trustworthiness of the inferred posteriors. Using measurements of the Cosmic Microwave Background (CMB), we show that TMNRE can achieve converged posteriors using orders of magnitude fewer simulator calls than conventional Markov Chain Monte Carlo (MCMC) methods. Remarkably, the required number of samples is effectively independent of the number of nuisance parameters. In addition, a property called \emph{local amortization} allows the performance of rigorous statistical consistency checks that are not accessible to sampling-based methods. TMNRE promises to become a powerful tool for cosmological data analysis, particularly in the context of extended cosmologies, where the timescale required for conventional sampling-based inference methods to converge can greatly exceed that of simple cosmological models such as $\Lambda$CDM. To perform these computations, we use an implementation of TMNRE via the open-source code \texttt{swyft}.
翻译:以抽样为基础的推论技术是现代宇宙数据分析的核心;然而,这些方法在规模上与维度不相称,通常需要近似或难测的可能性。在本文中,我们描述的是,“边际边际神经比估计”(TMNRE)(所谓的基于模拟推论的新方法)如何自然回避这些问题,提高了(一)美元的效率,(二)美元可缩放性,和(三)美元推导的后背体的可信度。使用对《哥斯摩微波背景》(CMB)的测量,我们表明,TMNRE能够使用比传统的马可夫链蒙特卡洛(MC)方法少的量级模拟器电话(TMNRE)来达到趋同的远似相。值得注意的是,所需的样品数量实际上独立于了“以模拟为基础的参数”的数量。此外,一个称为\emph{当地摊销性的属性使得能够进行严格的统计一致性检查,而基于取样的方法是无法获得的。TMNRE承诺成为以宇宙为基础的数据分析的强大工具,特别是在传统的摩数源模型中,可以大大地进行这种常规的代算的比。