AWhat changed
Estimates variational-objective gradients using samples and score functions, with model-structure-based Rao-Blackwellization and control variates to reduce model-specific derivations.
Scientific scope
Estimates variational-objective gradients using samples and score functions, with model-structure-based Rao-Blackwellization and control variates to reduce model-specific derivations. Requires computable log joint, variational sampling and score functions plus appropriate interchange/moment/step-size conditions. Unbiased stochastic gradients do not make an approximate variational posterior exact or guarantee finite-time/global optimization.
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 computable log joint, variational sampling and score functions plus appropriate interchange/moment/step-size conditions. Unbiased stochastic gradients do not make an approximate variational posterior exact or guarantee finite-time/global optimization.
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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