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Perfect simulation of positive Gaussian distributions
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“On the one hand, for the first time we have conducted the Bayesian inference using exact iid samples from the posterior and we did not have to fret about unquantifiable issues such as 'burn-in' and 'mixing-speed' as is typical with approximate MCMC simulation (Philippe and Robert, 2003).”
From The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting (2017) · cites this paper as Philippe and Robert, 2003
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Research works printed but not linked: 12
- Casella G., Lavine M., and Robert C.P. 2001. Explaining the perfect sampler. The American Statistician 55(4): 299–305. · doi:10.1198/000313001753272240
- Casella G., Mengersen K.L., Robert C.P., and Titterington D.M. 2002. Perfect samplers for mixtures of distributions. J. Royal Statist. Soc. (Ser. B) 64(4): 777–790. · doi:10.1111/1467-9868.00360
- Damien P., Wakefield J., and Walker S. 1999. Gibbs sampling for Bayesian non-conjugate and hierarchical models by using auxiliary variables. J. Royal Statist. Soc. (Ser. B) 61: 331–344. · DOI · Paper page
- Dimakos X. 2001. A guide to exact simulation. Intern. Statist. Rev. 69(1): 27–48. · doi:10.1111/j.1751-5823.2001.tb00478.x
- Geweke J. 1991. Efficient simulation from the multivariate normal and student t-distributions subject to linear constraints. Computer Sciences and Statistics: Proc. 23d Symp. Interface, pp. 571–577.
- Hajivassiliou V.A., McFadden D., and Ruud P.A. 1996. Simulation of multivariate normal rectangle probabilities and their derivatives: Theoretical and computational results. J. Econometrics 72: 85–134. · DOI · Paper page
- Hobert J.P., Robert C.P., and Titterington D.M. 1999. On perfect simulation for some mixtures of distributions. Statistics and Computing 9: 287–298. · DOI
- Mira A., Møller J., and Roberts G.O. 2001. Perfect slice samplers. J. Royal Statist. Soc. (Ser. B) 63(3): 593–606. · doi:10.1111/1467-9868.00301
- Propp J.G. and Wilson D.B. 1996. Exact sampling with coupled Markov chains and applications to statistical mechanics. Random Structures and Algorithms 9: 223–252. · DOI · Paper page
- Robert C.P. 1995. Simulation of truncated normal variables. Statistics and Computing 5: 121–125. · DOI · Paper page
- Robert C.P. and Casella G. 1999. Monte Carlo Statistical Methods. Springer Verlag, New York. · DOI · Paper page
- Wilson D.B. 1998. Annotated bibliography of perfectly random sampling with Markov chains. In: Aldous D. and Propp J. (Eds.), Microsurveys in Discrete Probability, Volume 41 of DIMACS Series in Discrete Mathematics and Theoretical Computer Science, American Mathematical Society, pp. 209–220. · doi:10.1090/dimacs/041/11
Cited by
The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting
Botev, Z. I. (2017) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/rssb.12162 · Cited by 247
A limit formula and recursive algorithm for multivariate Normal tail probability
Au, S.-K. (2025) · Statistics and Computing · doi:10.1007/s11222-024-10552-z
Metadata
- Authors
- Philippe, A., & Robert, C. P.
- Venue
- Statistics and Computing
- Published
- 2003-04
- Volume
- 13
- Issue
- 2
- Pages
- 179-186
- ISSN
- 0960-3174
- 1573-1375
- Cited by
- 22
- References
- 0
- Language
- en
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
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- Philippe, A., & Robert, C. P. (2003). Perfect simulation of positive Gaussian distributions. Statistics and Computing. https://doi.org/10.1023/a%3A1023264710933
Data sources
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.
