AWhat changed
Transforms constrained continuous latent variables to unconstrained coordinates, selects Gaussian variational families and uses reparameterized automatic-differentiation gradients for generic optimization.
Scientific scope
Transforms constrained continuous latent variables to unconstrained coordinates, selects Gaussian variational families and uses reparameterized automatic-differentiation gradients for generic optimization. Requires differentiable continuous-latent log densities and appropriate tractable transforms; discrete variables only fit when they can be tractably marginalized. Transformation and variational-family choices affect accuracy; no exact-posterior or arbitrary-model guarantee. Reparameterization, stochastic optimization and Stan autodiff are inherited.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Requires differentiable continuous-latent log densities and appropriate tractable transforms; discrete variables only fit when they can be tractably marginalized. Transformation and variational-family choices affect accuracy; no exact-posterior or arbitrary-model guarantee. Reparameterization, stochastic optimization and Stan autodiff are inherited.
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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