The Block-Poisson Estimator for Optimally Tuned Exact Subsampling MCMC-Reference-Cited by-同舟云学术

The Block-Poisson Estimator for Optimally Tuned Exact Subsampling MCMC

Published:2021-06-01 Issue:4 Volume:30 Page:877-888
ISSN:1061-8600
Container-title:Journal of Computational and Graphical Statistics
language:en
Short-container-title:Journal of Computational and Graphical Statistics

Author:

Quiroz Matias¹²,Tran Minh-Ngoc³,Villani Mattias⁴⁵^ORCID,Kohn Robert⁶,Dang Khue-Dung¹

Affiliation:

1. School of Mathematical and Physical Sciences, University of Technology Sydney, Ultimo, Australia

2. Research Division, Sveriges Riksbank, Stockholm, Sweden

3. Discipline of Business Analytics, University of Sydney, Sydney, Australia

4. Department of Statistics, Stockholm University, Stockholm, Sweden

5. Department of Computer and Information Science, Linköping University, Linköping, Sweden

6. School of Economics, UNSW Business School, University of New South Wales, Kensington, Australia

Funder

Stiftelsen för Strategisk Forskning

ARC Centre of Excellence, Mathematical and Statistical frontiers

Australian AustralianResearch Council Center of Excellence

Swedish Foundation for Strategic Research

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Discrete Mathematics and Combinatorics,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/10618600.2021.1917420

Reference36 articles.

1. The pseudo-marginal approach for efficient Monte Carlo computations

2. Searching for exotic particles in high-energy physics with deep learning

3. Accelerating Metropolis-Hastings algorithms by Delayed Acceptance

4. Bardenet, R., Doucet, A., and Holmes, C. (2014), “Towards Scaling up Markov Chain Monte Carlo: An Adaptive Subsampling Approach,” in Proceedings of The 31st International Conference on Machine Learning, pp. 405–413.

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