Implicit and explicit reparameterizations of the Folded Normal and Rice distributions
Variational inference is a flexible tool for modeling experimental data and has proven useful in crystallography. Pathwise gradient estimators provide low-variance gradients for reparameterized variational inference, but are not readily available for some distributions central to crystallography. This work derives and implements implicit and explicit reparameterized gradient estimators for the folded Normal and Rice distributions in \prog{PyTorch}, and compares their variance analytically and empirically. In each case the implicit estimator achieves lower variance for the location derivative, particularly at small location-to-scale ratios, while the scale-derivative variances are nearly e...
| Authors | Luis Aldama, Kevin Dalton, Minhuan Li et al. |
|---|---|
| Primary category | stat.ME |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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