Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference

Richard D. P. Grumitt; Biwei Dai; Uros Seljak

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arXiv · 2205.14240

Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference

Richard D. P. Grumitt·Biwei Dai·Uros Seljak

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Abstract

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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BibTeX RIS

Richard D. P. Grumitt, Biwei Dai, Uros Seljak. 2022-10-13. Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference. https://arxiv.org/abs/2205.14240

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