Fisher-Bingham-like normalizing flows on the sphere
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Abstract (arXiv)
A generic D-dimensional Gaussian can be conditioned or projected onto the D-1 unit sphere, thereby leading to the well-known Fisher-Bingham (FB) or Angular Gaussian (AG) distribution families, respectively. These are some of the most fundamental distributions on the sphere, yet cannot straightforwardly be written as a normalizing flow except in two special cases: the von-Mises Fisher in D=3 and the central angular Gaussian in any D. In this paper, we describe how to generalize these special cases to a family of normalizing flows that behave similarly to the full FB or AG family in any D. We call them "zoom-linear-project" (ZLP)-Fisher flows. Unlike a normal Fisher-Bingham distribution, their composition allows to gradually add complexity as needed. Furthermore, they can naturally handle conditional density estimation with target distributions that vary by orders of magnitude in scale - a setting that is important in astronomical applications but that existing flows often struggle with. A particularly useful member of the new family is the Kent analogue that can cheaply upgrade any flow in this situation to yield better performance.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 24 references that are in the map. First posted on arXiv 6 Oct 2025.
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7 of 24 references.
References · 7
- 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,539
- 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
- 2019Neural Spline Flowscited by 590
- 2019Automatic Posterior Transformation for Likelihood-Free Inferencecited by 216
- 2018Hyperspherical Variational Auto-Encoderscited by 188
- 2020Normalizing Flows on Tori and Spherescited by 117
- 2022Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Modelscited by 50
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