Building on the recent trend of new deep generative models known as Normalizing Flows (NF), simulation-based inference (SBI) algorithms can now efficiently accommodate arbitrary complex and high-dimensional data distributions. The development of appropriate validation methods however has fallen behind. Indeed, most of the existing metrics either require access to the true posterior distribution, or fail to provide theoretical guarantees on the consistency of the inferred approximation beyond the one-dimensional setting. This work proposes easy to interpret validation diagnostics for multi-dimensional conditional (posterior) density estimators based on NF. It also offers theoretical guarantees based on results of local consistency. The proposed workflow can be used to check, analyse and guarantee consistent behavior of the estimator. The method is illustrated with a challenging example that involves tightly coupled parameters in the context of computational neuroscience. This work should help the design of better specified models or drive the development of novel SBI-algorithms, hence allowing to build up trust on their ability to address important questions in experimental science.
翻译:根据被称为 " 正常流动(NF) " 的新的深层基因模型的最近趋势,基于模拟的推论(SBI)算法现在可以有效地适应任意的复杂和高维的数据分布。然而,适当的验证方法的开发已经落后。事实上,大多数现有的衡量标准要么需要获取真实的后天分布,要么没有为单维设置以外的推论近似的一致性提供理论保证。这项工作建议很容易地解释基于NF的多维条件(其他)密度测算器的验证诊断。它也提供了基于地方一致性结果的理论保证。拟议的工作流程可用于检查、分析和保证估测器的一致行为。该方法的例子具有挑战性,涉及计算神经科学方面的紧密结合参数。这项工作应有助于设计更精确的模型或推动开发新的SBI-algorithms,从而能够建立对其在实验科学中处理重要问题的能力的信任。