The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Delayed rejection in reversible jump Metropolis-Hastings
Abstract
In a Metropolis–Hastings algorithm, rejection of proposed moves is an intrinsic part of ensuring that the chain converges to the intended target distribution. However, persistent rejection, perhaps in particular parts of the state space, may indicate that locally the proposal distribution is badly calibrated to the target. As an alternative to careful off-line tuning of state-dependent proposals, the basic algorithm can be modified so that, on rejection, a second attempt to move is made. A different proposal can be generated from a new distribution that is allowed to depend on the previously rejected proposal. We generalise this idea of delaying the rejection and adapting the proposal distribution, due to Tierney & Mira (1999), to generate a more flexible class of methods that applies in particular to a variable-dimension setting. The approach is illustrated by two pedagogical examples and a more realistic application to a changepoints analysis for point processes.
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Citations
Cites
Monte Carlo sampling methods using Markov chains and their applications.
Hastings, W. K. (1970) · Biometrika · doi:10.1093/biomet/57.1.97 · Cited by 15,195
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
GREEN, P. J. (1995) · Biometrika · doi:10.1093/biomet/82.4.711 · Cited by 5,930
Self-citationWeak convergence and optimal scaling of random walk Metropolis algorithms
Roberts, G. O., Gelman, A., & Gilks, W. R. (1997) · The Annals of Applied Probability · doi:10.1214/aoap/1034625254 · Cited by 1,693
(1991) · University of Minnesota Digital Conservancy (University of Minnesota) · Cited by 1,077
Optimum Monte-Carlo sampling using Markov chains
PESKUN, P. H. (1973) · Biometrika · doi:10.1093/biomet/60.3.607 · Cited by 531
A note on Metropolis-Hastings kernels for general state spaces
Tierney, L. (1998) · The Annals of Applied Probability · doi:10.1214/aoap/1027961031 · Cited by 338
Some adaptive Monte Carlo methods for Bayesian inference
Tierney, L., & Mira, A. (1999) · Statistics in Medicine · doi:10.1002/(sici)1097-0258(19990915/30)18:17/18<2507::aid-sim272>3.0.co;2-j · Cited by 293
Self-citationOn Metropolis-Hastings algorithms with delayed rejection
Mira, A. (2001) · RePEc: Research Papers in Economics · Cited by 158
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Cited by
Dealing with uncertainty in model updating for damage assessment: a review
Simoen, E., De Roeck, G., & Lombaert, G. (2015) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2014.11.001 · Cited by 540
Simulation-based optimal bayesian experimental design for nonlinear systems
Huan, X., & Marzouk, Y. M. (2013) · Journal of Computational Physics · doi:10.1016/j.jcp.2012.08.013 · Cited by 438
“Two useful improvements to MH are the concepts of delayed rejection (DR) [77, 78] and adaptive Metropolis (AM) [79]; combining these lead to the DRAM algorithm of Haario et al.”
Method
Metadata
- Authors
- Green, P. J., & Mira, A.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 2001-12-01
- Volume
- 88
- Issue
- 4
- Pages
- 1035-1053
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2114350394
- Cited by
- 263
- References
- 8
- FWCI
- 4.91
- Type
- journal article
- Language
- en
- Open access
- closed
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