What is recorded here?
Estimates variational-objective gradients using samples and score functions, with model-structure-based Rao-Blackwellization and control variates to reduce model-specific derivations.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
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.
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People and systems
Rajesh Ranganath; Sean Gerrish; David M. Blei
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.
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English catalogue narrative
Black Box Variational Inference score-gradient automation
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
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.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Rajesh Ranganath; Sean Gerrish; David M. Blei
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.