Simulation-based inference with conditional neural density estimators is a powerful approach to solving inverse problems in science. However, these methods typically treat the underlying forward model as a black box, with no way to exploit geometric properties such as equivariances. Equivariances are common in scientific models, however integrating them directly into expressive inference networks (such as normalizing flows) is not straightforward. We here describe an alternative method to incorporate equivariances under joint transformations of parameters and data. Our method -- called group equivariant neural posterior estimation (GNPE) -- is based on self-consistently standardizing the "pose" of the data while estimating the posterior over parameters. It is architecture-independent, and applies both to exact and approximate equivariances. As a real-world application, we use GNPE for amortized inference of astrophysical binary black hole systems from gravitational-wave observations. We show that GNPE achieves state-of-the-art accuracy while reducing inference times by three orders of magnitude.
翻译:以有条件神经密度估计器进行模拟推断是解决科学中反面问题的有力方法。 但是,这些方法通常将基础前方模型作为黑盒处理,没有办法利用等差等等几何特性。 等差在科学模型中很常见, 但是将它们直接纳入直表性推断网络( 如正常流) 并不简单。 我们在这里描述一种将等差纳入参数和数据联合转换的替代方法。 我们的方法 -- -- 称为群体等异性神经后向估计( 国产总值) -- -- 以自我一致地将数据“ 位置” 标准化为基础,同时估计远差参数。 它是建筑独立的, 适用于精确和大致的等差。 作为现实世界应用, 我们使用国产总值来从重力波观测中推导出天体物理双黑洞系统。 我们显示,国产总值在用三等量的量级缩小时实现了状态的精确度。