An Analysis of Transformed Unadjusted Langevin Algorithm for Heavy-Tailed Sampling-Reference-Cited by-同舟云学术

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An Analysis of Transformed Unadjusted Langevin Algorithm for Heavy-Tailed Sampling

Published:2024-01 Issue:1 Volume:70 Page:571-593
ISSN:0018-9448
Container-title:IEEE Transactions on Information Theory
language:
Short-container-title:IEEE Trans. Inform. Theory

Author:

He Ye¹^ORCID,Balasubramanian Krishnakumar²^ORCID,Erdogdu Murat A.³

Affiliation:

1. School of Mathematics, Georgia Institute of Technology, Atlanta, GA, USA

2. Department of Statistics, University of California at Davis, Davis, CA, USA

3. Department of Computer Science and the Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada

Funder

NSF Transdisciplinary Research in Principles of Data Science

NSF

Natural Sciences and Engineering Research Council of Canada

Connaught New Researcher Award

Canadian Institute for Advanced Research (CIFAR), Artificial Intelligence (AI) Chairs Program

CIFAR AI Catalyst Grant

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Library and Information Sciences,Computer Science Applications,Information Systems

Link

https://ieeexplore.ieee.org/ielam/18/10375320/10258391-aam.pdf

Reference68 articles.

1. Multivariate T-Distributions and Their Applications

2. Convergence of Heavy-tailed Monte Carlo Markov Chain Algorithms

3. Efficient strategy for the Markov chain Monte Carlo in high-dimension with heavy-tailed target probability distribution

4. Approximations to multivariate $t$ integrals with application to multiple comparison procedures

5. Computation of Multivariate Normal and t Probabilities

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