Sequential neural posterior estimation (SNPE) techniques have been recently proposed for dealing with simulation-based models with intractable likelihoods. Unlike approximate Bayesian computation, SNPE techniques learn the posterior from sequential simulation using neural network-based conditional density estimators. This paper reclaims SNPE-B proposed by Lueckmann et al. (2017), which suffers from inefficiency and slow inference due to inefficient utilization of simulated data and high variance of parameter updates. To address these issues, we firstly introduce a concentrated loss function based on an adaptive calibration kernel that reweights the simulated data appropriately to improve the data efficiency. Moreover, we provide a theoretical analysis of the variance of associated Monte Carlo estimators. Based on this analysis, we then propose several variance reduction techniques to further accelerate the process of learning. Numerical experiments demonstrate that our method outperforms the original method together with other existing competitors on certain tasks.

翻译：序贯神经后验估计（SNPE）技术是近年提出的用于处理不可计算似然的仿真模型推断方法。与近似贝叶斯计算不同，SNPE通过基于神经网络的条件密度估计器，从序贯仿真中学习后验分布。本文重新审视了Lueckmann等人（2017）提出的SNPE-B方法，该方法因仿真数据利用效率低及参数更新方差大，存在推断效率低下与收敛缓慢的问题。为克服这些局限，我们首先引入基于自适应校准核的集中损失函数，通过合理重加权仿真数据提升数据利用效率。此外，我们对相关蒙特卡洛估计量的方差进行了理论分析，并在此基础上提出多种方差缩减技术以进一步加速学习过程。数值实验表明，在特定任务中，我们的方法较原始方法及其他现有竞争方法具有更优性能。