Phantom types for robust hierarchical models with typegeist
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Shared by Gary on Nov 1, 2025 • 12:55 AM UTC
NobleBlocks IDNB5481546A
Authors:
Daniel O’Hanlon
Abstract
In Bayesian hierarchical models, group-level parameter arrays must be mapped to the observationaxis, oftenusingexplicitindexing. Incomplexmodelswithnumerousincompatible data and parameter sets, this introduces the potential for bugs, as indexing with the incorrect indices typically fails silently. H...
Highlights
-Phantom types are introduced to enforce data-parameter-index correspondences in hierarchical models.
-Implemented in the typegeist Python package and compatible with Pyro, NumPyro, PyMC, TensorFlow Probability.
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