An adaptively weighted stochastic gradient MCMC algorithm for Monte Carlo simulation and global optimization-Reference-Cited by-同舟云学术

An adaptively weighted stochastic gradient MCMC algorithm for Monte Carlo simulation and global optimization

Published:2022-07-09 Issue:4 Volume:32 Page:
ISSN:0960-3174
Container-title:Statistics and Computing
language:en
Short-container-title:Stat Comput

Author:

Deng Wei,Lin Guang,Liang Faming^ORCID

Funder

National Science Foundation

National Institutes of Health

U.S. Department of Energy

Brookhaven National Laboratory

Publisher

Springer Science and Business Media LLC

Subject

Computational Theory and Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability,Theoretical Computer Science

Link

https://link.springer.com/content/pdf/10.1007/s11222-022-10120-3.pdf

Reference67 articles.

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3. Aitchison, L.: A statistical theory of cold posteriors in deep neural networks. In: International Conference on Learning Representation (ICLR) (2021)

4. Andrieu, C., Moulines, E., Priouret, P.: Stability of stochastic approximation under verifiable conditions. SIAM J. Control Optim. 44, 283–312 (2005)

5. Belkin, M.: Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation. Acta Numer. 30, 203–248 (2021)

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