We present Sequential Neural Likelihood (SNL), a new method for Bayesian
inference in simulator models, where the likelihood is intractable but
simulating data from the model is possible. SNL trains an autoregressive flow
on simulated data in order to learn a model of the likelihood in the region of
high posterior density. A sequential training procedure guides simulations and
reduces simulation cost by orders of magnitude. We show that SNL is more
robust, more accurate and requires less tuning than related neural-based
methods, and we discuss diagnostics for assessing calibration, convergence and
goodness-of-fit.

本研究提出了顺序神经可能性方法（SNL），这是一种用于贝叶斯推断的新方法，适用于具有难以计算可能性的模拟器模型，其中可以从模型中模拟数据。SNL 在模拟数据上训练自回归流，以学习高后验密度区域可能性的模型。该论文表明，相对于相关的基于神经网络方法，SNL 更加健壮，更准确并且需要较少的调整，并且讨论了用于评估校准，收敛和拟合优度的诊断方法。