NeurIPS 2022accept10 citations
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference
Richard D.P. Grumitt, Biwei Dai, Uros Seljak
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.
probabilistic methodsBayesian InferenceNormalizing Flows
BibTeX
@inproceedings{
grumitt2022deterministic,
title={Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference},
author={Richard D.P. Grumitt and Biwei Dai and Uros Seljak},
booktitle={Advances in Neural Information Processing Systems},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022},
url={https://openreview.net/forum?id=kEPAmGivMD}
}