Bayesian inference without the access of likelihood, or likelihood-free inference, has been a key research topic in simulations, to yield a more realistic generation result. Recent likelihood-free inference updates an approximate posterior sequentially with the dataset of the cumulative simulation input-output pairs over inference rounds. Therefore, the dataset is gathered through the iterative simulations with sampled inputs from a proposal distribution by MCMC, which becomes the key of inference quality in this sequential framework. This paper introduces a new proposal modeling, named as Implicit Surrogate Proposal (ISP), to generate a cumulated dataset with further sample efficiency. ISP constructs the cumulative dataset in the most diverse way by drawing i.i.d samples via a feed-forward fashion, so the posterior inference does not suffer from the disadvantages of MCMC caused by its non-i.i.d nature, such as auto-correlation and slow mixing. We analyze the convergence property of ISP in both theoretical and empirical aspects to guarantee that ISP provides an asymptotically exact sampler. We demonstrate that ISP outperforms the baseline inference algorithms on simulations with multi-modal posteriors.

翻译：在模拟中,没有可能性或无可能性推断的贝叶斯推论一直是关键的研究课题,以产生更现实的生成结果。最近没有可能性的推论以累积模拟投入-产出对对数的数据集相继更新了一个近似后部,因此,数据集是通过迭代模拟收集的,由监测监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测和评价等建议分发的抽样投入收集出来的。本文介绍了一个新的提案模型,称为隐性代谢提议(ISP),以产生具有进一步取样效率的累积数据集。ISP以最多样化的方式,通过进料-前向方式绘制i.i.d样本,以构建累积数据集,因此,后部推论不会因非一.d性质(如自动调节和缓慢混合)而使监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、监测、