The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Coupling and Ergodicity of Adaptive Markov Chain Monte Carlo Algorithms
Abstract
We consider basic ergodicity properties of adaptive Markov chain Monte Carlo algorithms under minimal assumptions, using coupling constructions. We prove convergence in distribution and a weak law of large numbers. We also give counterexamples to demonstrate that the assumptions we make are not redundant.
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Cites
A Stochastic Approximation Method
Robbins, H., & Monro, S. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729586 · Cited by 9,643
Weak 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
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Cited by
Vrugt, J. A. (2016) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2015.08.013 · Cited by 851
Sampling methods for solving Bayesian model updating problems: A tutorial
Lye, A., Cicirello, A., & Patelli, E. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.107760 · Cited by 169
Adaptive Metropolis algorithm using variational Bayesian adaptive Kalman filter
Mbalawata, I. S., Särkkä, S., Vihola, M., & Haario, H. (2015) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2014.10.006 · Cited by 54
Metadata
- Authors
- Roberts, G. O., & Rosenthal, J. S.
- Venue
- Journal of Applied Probability
- Publisher
- Cambridge University Press (CUP)
- Published
- 2007-06
- Volume
- 44
- Issue
- 2
- Pages
- 458-475
- ISSN
- 0021-9002 (ISSN-L)
- 1475-6072
- OpenAlex
- W2006582998
- Cited by
- 362
- References
- 2
- FWCI
- 12.61
- Type
- journal article
- Language
- en
- Open access
- closed
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- Roberts, G. O., & Rosenthal, J. S. (2007). Coupling and Ergodicity of Adaptive Markov Chain Monte Carlo Algorithms. Journal of Applied Probability. https://doi.org/10.1239/jap/1183667414
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
