AWhat changed
Embeds dynamic probabilistic models in Python/PyTorch with sample/parameter primitives and composable Poutine effect handlers, supporting scalable approximate inference.
Scientific scope
Embeds dynamic probabilistic models in Python/PyTorch with sample/parameter primitives and composable Poutine effect handlers, supporting scalable approximate inference. Universal language expressivity does not make arbitrary posterior inference tractable or exact. Practical stochastic variational inference and user-specified guides are approximate. NUTS, SVI, automatic differentiation and effect-handler concepts are inherited; v1's journal header is an unfinished template, not proof of a 2018 journal publication.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Universal language expressivity does not make arbitrary posterior inference tractable or exact. Practical stochastic variational inference and user-specified guides are approximate. NUTS, SVI, automatic differentiation and effect-handler concepts are inherited; v1's journal header is an unfinished template, not proof of a 2018 journal publication.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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