nbi: the Astronomer's Package for Neural Posterior Estimation
Keming Zhang, Joshua S. Bloom, St\'efan van der Walt, Nina Hernitschek

TL;DR
The paper introduces nbi, an open-source software framework that enhances neural posterior estimation for astronomy by providing built-in featurizers and an asymptotically exact inference algorithm, facilitating routine application to light curves and spectra.
Contribution
It presents a new framework and software, nbi, with built-in featurizers and a modified SNPE-IS algorithm for more accurate and user-friendly neural posterior estimation in astronomy.
Findings
nbi supports off-the-shelf application to astronomical data
Built-in featurizers improve handling of sequential data
SNPE-IS achieves asymptotically exact inference
Abstract
Despite the promise of Neural Posterior Estimation (NPE) methods in astronomy, the adaptation of NPE into the routine inference workflow has been slow. We identify three critical issues: the need for custom featurizer networks tailored to the observed data, 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 nbi (Neural Bayesian Inference), which supports both amortized and sequential NPE. First, nbi provides built-in "featurizer" networks with demonstrated efficacy on sequential data, such as light curve and spectra, thus obviating the need for this customization on the user end. Second, we introduce a modified algorithm SNPE-IS, which facilities asymptotically exact inference by using the surrogate posterior under NPE only as a proposal distribution for importance…
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Taxonomy
TopicsTraditional Chinese Medicine Studies · Metabolomics and Mass Spectrometry Studies · Advanced Statistical Methods and Models
