Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference
Machine Learning
2022-10-14 v2 Statistical Mechanics
Machine Learning
Data Analysis, Statistics and Probability
Computation
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.
Cite
@article{arxiv.2205.14240,
title = {Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference},
author = {Richard D. P. Grumitt and Biwei Dai and Uros Seljak},
journal= {arXiv preprint arXiv:2205.14240},
year = {2022}
}
Comments
17 pages, 9 figures, Accepted at NeurIPS 2022