State-of-the-art neural network-based methods for learning summary statistics have delivered promising results for simulation-based likelihood-free parameter inference. Existing approaches require density estimation as a post-processing step building upon deterministic neural networks, and do not take network prediction uncertainty into account. This work proposes a robust integrated approach that learns summary statistics using Bayesian neural networks, and directly estimates the posterior density using categorical distributions. An adaptive sampling scheme selects simulation locations to efficiently and iteratively refine the predictive posterior of the network conditioned on observations. This allows for more efficient and robust convergence on comparatively large prior spaces. We demonstrate our approach on benchmark examples and compare against related methods.

翻译：现有方法要求将密度估计作为后处理步骤,以确定性神经网络为基础,而不考虑网络预测的不确定性。这项工作提出了一种强有力的综合方法,利用巴伊西亚神经网络学习简要统计数据,并使用绝对分布直接估计后方密度。适应性抽样方案选择模拟地点,以高效和迭接的方式完善网络的预测后后方,并以观察为条件。这样,就可以在相对大的先前空间上更有效和有力地趋同。我们展示了我们的基准范例,并与相关方法进行比较。