Workshop: 2nd ICML Workshop on Machine Learning for Astrophysics

nbi: the Astronomer's Package for Neural Posterior Estimation

Keming Zhang · Josh Bloom


Despite the growing popularity of Neural Posterior Estimation (NPE) methods in astronomy, the adaptation of such technique into routine data analysis has been slow. We identify three critical issues: the steep learning curve of NPE for domain scientists, the inference inexactness, and the under-specification of physical forward models. To address the first two issues, we introduce a new framework and open-source software \textit{nbi}: Neural Bayesian Inference, which implements both amortized and sequential NPE.First, \textit{nbi} provides built-in ``featurizer'' networks with demonstrated efficacy on sequential data, such as light curve and spectra, thus eliminating the need for customization on the user end. Second, we introduce a modified algorithm SNPE-IS, which facilities asymptotically exact inference by using the surrogate posterior only as a proposal distribution for importance sampling.These features allow \textit{nbi} to be applied off-the-shelf to astronomical inference problems involving light curves and spectra, which would otherwise be tackled with MCMC and Nested Sampling. Our package\footnote{Anonymized for review.} is at \url{}. An application paper is concurrently submitted to this workshop and included in the appendix for reviewing purposes.

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