Improved estimation of relaxation time in nonreversible Markov chains
Author:
Affiliation:
1. Center for AI Project, RIKEN
2. Department of Computer Science, Ben–Gurion University of the Negev
Publisher
Institute of Mathematical Statistics
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Reference53 articles.
1. Bradley, R. C. (2005). Basic properties of strong mixing conditions. A survey and some open questions. Probab. Surv. 2 107–144.
2. YU, B. (1994). Rates of convergence for empirical processes of stationary mixing sequences. Ann. Probab. 22 94–116.
3. FILL, J. A. (1991). Eigenvalue bounds on convergence to stationarity for nonreversible Markov chains, with an application to the exclusion process. Ann. Appl. Probab. 1 62–87.
4. SYED, S., BOUCHARD-CÔTÉ, A., DELIGIANNIDIS, G. and DOUCET, A. (2022). Non-reversible parallel tempering: A scalable highly parallel MCMC scheme. J. R. Stat. Soc. Ser. B. Stat. Methodol. 84 321–350.
5. STEINWART, I., HUSH, D. and SCOVEL, C. (2009). Learning from dependent observations. J. Multivariate Anal. 100 175–194.
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