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Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference
- Publication Year :
- 2022
-
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.<br />Comment: 17 pages, 9 figures, Accepted at NeurIPS 2022
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2205.14240
- Document Type :
- Working Paper