Gradient estimation of probabilistic programs reduces soundly to probabilistic inference after programmable coupling and factorization, enabling new low-variance estimators that beat baselines.
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2 Pith papers cite this work, alongside 94 external citations. Polarity classification is still indexing.
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A pipeline combining importance sampling with Markov state models, chain-rule sensitivities, and RiteWeight reweighting enables efficient parameter optimization for rare-event dynamics in nonequilibrium systems.
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GradInf: Gradient Estimation as Probabilistic Inference
Gradient estimation of probabilistic programs reduces soundly to probabilistic inference after programmable coupling and factorization, enabling new low-variance estimators that beat baselines.
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Sensitivity Analysis in the Face of Rare Events
A pipeline combining importance sampling with Markov state models, chain-rule sensitivities, and RiteWeight reweighting enables efficient parameter optimization for rare-event dynamics in nonequilibrium systems.