A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions.

ABSTRACT: We propose an adaptively weighted stochastic gradient Langevin dynamics algorithm (SGLD), so-called contour stochastic gradient Langevin dynamics (CSGLD), for Bayesian learning in big data statistics. The proposed algorithm is essentially a scalable dynamic importance sampler, which automatically flattens the target distribution such that the simulation for a multi-modal distribution can be greatly facilitated. Theoretically, we prove a stability condition and establish the asymptotic convergence of the self-adapting parameter to a unique fixed-point, regardless of the non-convexity of the original energy function; we also present an error analysis for the weighted averaging estimators. Empirically, the CSGLD algorithm is tested on multiple benchmark datasets including

SUBMITTER: Deng W 

PROVIDER: S-EPMC8457681 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature