Stochastic zeroth-order discretizations of Langevin diffusions for Bayesian inference-Reference-Cited by-同舟云学术

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Stochastic zeroth-order discretizations of Langevin diffusions for Bayesian inference

Published:2022-08-01 Issue:3 Volume:28 Page:
ISSN:1350-7265
Container-title:Bernoulli
language:
Short-container-title:Bernoulli

Author:

Roy Abhishek¹,Shen Lingqing²,Balasubramanian Krishnakumar¹,Ghadimi Saeed³

Affiliation:

1. Department of Statistics, University of California, Davis, Davis, CA 95616, USA

2. Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA 15213

3. Department of Management Sciences, University of Waterloo, Waterloo, ON N2L 3G1, Canada

Publisher

Bernoulli Society for Mathematical Statistics and Probability

Subject

Statistics and Probability

Reference80 articles.

1. Dalalyan, A.S. (2017). Theoretical guarantees for approximate sampling from smooth and log-concave densities. J. R. Stat. Soc. Ser. B. Stat. Methodol. 79 651–676. 10.1111/rssb.12183

2. Dalalyan, A.S. (2017). Theoretical guarantees for approximate sampling from smooth and log-concave densities. J. R. Stat. Soc. Ser. B. Stat. Methodol. 79 651–676. 10.1111/rssb.12183

3. Dalalyan, A.S. and Karagulyan, A. (2019). User-friendly guarantees for the Langevin Monte Carlo with inaccurate gradient. Stochastic Process. Appl. 129 5278–5311. 10.1016/j.spa.2019.02.016

4. Dalalyan, A.S. and Riou-Durand, L. (2020). On sampling from a log-concave density using kinetic Langevin diffusions. Bernoulli 26 1956–1988. 10.3150/19-BEJ1178

5. Durmus, A. and Moulines, É. (2017). Nonasymptotic convergence analysis for the unadjusted Langevin algorithm. Ann. Appl. Probab. 27 1551–1587. 10.1214/16-AAP1238

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1. Stochastic zeroth-order discretizations of Langevin diffusions for Bayesian inference;Bernoulli;2022-08-01

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