Langevin Monte Carlo driven by a localized, structurally regularized, debiased score-matching network approximates intractable posteriors with fewer simulations and tighter intervals than standard SBI methods.
Return {θ(k)}K k=1 as approximated posterior samples In the actual implementation, we randomly partition the data into a training set (50%) and a validation set (50%)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
stat.ME 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Simulation-based Inference via Langevin Dynamics with Score Matching
Langevin Monte Carlo driven by a localized, structurally regularized, debiased score-matching network approximates intractable posteriors with fewer simulations and tighter intervals than standard SBI methods.