Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference

Richard Grumitt · Biwei Dai · Uros Seljak

Hall J #1034

Keywords: [ Probabilistic Methods ] [ bayesian inference ] [ normalizing flows ]

[ Abstract ]
[ Poster [ OpenReview
Thu 1 Dec 2 p.m. PST — 4 p.m. PST
Spotlight presentation: Lightning Talks 2A-4
Tue 6 Dec 6:30 p.m. PST — 6:45 p.m. PST


We propose a general purpose Bayesian inference algorithm for expensive likelihoods, replacing the stochastic term in the Langevin equation with a deterministic density gradient term. The particle density is evaluated from the current particle positions using a Normalizing Flow (NF), which is differentiable and has good generalization properties in high dimensions. We take advantage of NF preconditioning and NF based Metropolis-Hastings updates for a faster convergence. We show on various examples that the method is competitive against state of the art sampling methods.

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