Poster
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference
Richard Grumitt · Biwei Dai · Uros Seljak
Hall J (level 1) #1034
Keywords: [ Probabilistic Methods ] [ bayesian inference ] [ normalizing flows ]
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.