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Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference
Richard Grumitt · Biwei Dai · Uros Seljak

Tue Dec 06 05:00 PM -- 07:00 PM (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.

Author Information

Richard Grumitt (Tsinghua University)
Biwei Dai (UC Berkeley)
Uros Seljak (University of California Berkeley)

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