Simulation based inference (SBI) methods enable the estimation of posterior
distributions when the likelihood function is intractable, but where model
simulation is feasible. Popular neural approaches to SBI are the neural
posterior estimator (NPE) and its sequential version (SNPE). These methods can
outperform statistical SBI approaches such as approximate Bayesian computation
(ABC), particularly for relatively small numbers of model simulations. However,
we show in this paper that the NPE methods are not guaranteed to be highly
accurate, even on problems with low dimension. In such settings the posterior
cannot be accurately trained over the prior predictive space, and even the
sequential extension remains sub-optimal. To overcome this, we propose
preconditioned NPE (PNPE) and its sequential version (PSNPE), which uses a
short run of ABC to effectively eliminate regions of parameter space that
produce large discrepancy between simulations and data and allow the posterior
emulator to be more accurately trained. We present comprehensive empirical
evidence that this melding of neural and statistical SBI methods improves
performance over a range of examples, including a motivating example involving
a complex agent-based model applied to real tumour growth data.

模拟推理方法及其神经网络算法在处理具有数值模拟可行性但似然函数难以计算的问题中涉及到的后验分布估计。尽管现有方法在处理低维问题时表现出色，但本文提出的先决条件神经后验估计方法通过结合近似贝叶斯计算方法有效地改善了对大参数空间下的模型的后验分布训练精度。