Nested Rˆ: Assessing the Convergence of Markov Chain Monte Carlo When Running Many Short Chains-Reference-Cited by-同舟云学术

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Nested Rˆ: Assessing the Convergence of Markov Chain Monte Carlo When Running Many Short Chains

Published:2024-01-01 Issue:-1 Volume:-1 Page:
ISSN:1936-0975
Container-title:Bayesian Analysis
language:
Short-container-title:Bayesian Anal.

Author:

Margossian Charles C.¹,Hoffman Matthew D.²,Sountsov Pavel²,Riou-Durand Lionel³,Vehtari Aki⁴,Gelman Andrew⁵

Affiliation:

1. Center for Computational Mathematics, Flatiron Institute

2. Google Research

3. Laboratoire de Mathématiques de l’INSA Rouen Normandie

4. Department of Computer Science, Aalto University

5. Department of Statistics and Political Science, Columbia University

Publisher

Institute of Mathematical Statistics

Reference54 articles.

1. Roberts, G. O. and Rosenthal, J. S. (2004). “General state space Markov chains and MCMC algorithms.” Probability Surveys, 1: 20–71.

2. Flegal, J. M., Haran, M., and Jones, G. L. (2008). “Markov chain Monte Carlo: Can we trust the third significant figure?” Statistical Science, 250–260.

3. Neal, R. M. (2001). “Annealed importance sampling.” Statistics and Computing, 11: 125–139.

4. Beskos, A., Pillai, N., Roberts, G., Sanz-Serna, J.-M., and Stuart, A. (2013). “Optimal tuning of the hybrid Monte Carlo algorithm.” Bernoulli, 19(5A): 1501–1534.

5. Hoffman, M. D. and Gelman, A. (2014). “The no-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo.” Journal of Machine Learning Research, 15: 1593–1623.

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