Regularized Zero-Variance Control Variates-Reference-Cited by-同舟云学术

Regularized Zero-Variance Control Variates

Published:2022-01-01 Issue:-1 Volume:-1 Page:
ISSN:1936-0975
Container-title:Bayesian Analysis
language:
Short-container-title:Bayesian Anal.

Author:

South L. F.¹,Oates C. J.²,Mira A.³,Drovandi C.⁴

Affiliation:

1. School of Mathematical Sciences, Queensland University of Technology, Australia ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS)

2. School of Mathematics, Statistics and Physics, Newcastle University, UK Alan Turing Institute, UK

3. Faculty of Economics, Università della Svizzera italiana, Switzerland University of Insubria, Italy

4. School of Mathematical Sciences, Queensland University of Technology, Australia ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS)

Publisher

Institute of Mathematical Statistics

Subject

Applied Mathematics,Statistics and Probability

Reference59 articles.

1. Salomone, R., South, L. F., Drovandi, C. C., and Kroese, D. P. (2018). “Unbiased and consistent nested sampling via sequential Monte Carlo.” arXiv preprint arXiv:1805.03924.

2. Assaraf, R. and Caffarel, M. (1999). “Zero-Variance Principle for Monte Carlo Algorithms.” Physical Review Letters, 83(23): 4682–4685.

3. Baker, J., Fearnhead, P., Fox, E. B., and Nemeth, C. (2019). “Control Variates for Stochastic Gradient MCMC.” Statistics and Computing, 29(3): 599–615.

4. Barp, A., Oates, C. J., Porcu, E., and Girolami, M. (2022). “A Riemann-Stein Kernel Method.” Bernoulli, 28(4): 2181–2208. arXiv preprint 1810.04946.

5. Belkin, M., Hsu, D., and Xu, J. (2019). “Two models of double descent for weak features.” arXiv preprint arXiv:1903.07571.

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