The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Reversible jump Markov chain Monte Carlo computation and Bayesian model determination
Abstract
Markov chain Monte Carlo methods for Bayesian computation have until recently been restricted to problems where the joint distribution of all variables has a density with respect to some fixed standard underlying measure. They have therefore not been available for application to Bayesian model determination, where the dimensionality of the parameter vector is typically not fixed. This paper proposes a new framework for the construction of reversible Markov chain samplers that jump between parameter subspaces of differing dimensionality, which is flexible and entirely constructive. It should therefore have wide applicability in model determination problems. The methodology is illustrated with applications to multiple change-point analysis in one and two dimensions, and to a Bayesian comparison of binomial experiments.
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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
Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window
Madigan, D., & Raftery, A. E. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476894 · Cited by 1,205 · cited 1×
“…the posterior p(k, O(k)ly) as the objective of the computation does not preclude model selection or prediction being ultimately based on a non-coherent principle such as that advocated by Madigan & Raftery (1994); thus the methods of the present paper would be applicable to their analysis.”
Method
Cited by
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Inference of Population Structure Using Multilocus Genotype Data
Pritchard, J. K., Stephens, M., & Donnelly, P. (2000) · Genetics · doi:10.1093/genetics/155.2.945 · Cited by 33,908
MRBAYES: Bayesian inference of phylogenetic trees.
Huelsenbeck, J. P., & Ronquist, F. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.8.754 · Cited by 21,937
Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999) · Statistical Science · doi:10.1214/ss/1009212519 · Cited by 4,118
Posada, D., & Buckley, T. R. (2004) · Systematic Biology · doi:10.1080/10635150490522304 · Cited by 3,927
Metadata
- Authors
- GREEN, P. J.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1995
- Volume
- 82
- Issue
- 4
- Pages
- 711-732
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2106706098
- Cited by
- 5,930
- References
- 2
- FWCI
- 37.31
- Type
- journal article
- Language
- en
- Open access
- closed
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- GREEN, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika. https://doi.org/10.1093/biomet/82.4.711
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
