The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
The pseudo-marginal approach for efficient Monte Carlo computations
Open accessAbstract
We introduce a powerful and flexible MCMC algorithm for stochastic simulation. The method builds on a pseudo-marginal method originally introduced in [Genetics 164 (2003) 1139–1160], showing how algorithms which are approximations to an idealized marginal algorithm, can share the same marginal stationary distribution as the idealized method. Theoretical results are given describing the convergence properties of the proposed method, and simple numerical examples are given to illustrate the promising empirical characteristics of the technique. Interesting comparisons with a more obvious, but inexact, Monte Carlo approximation to the marginal algorithm, are also given.
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Cites
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
Monte Carlo Statistical Methods
Kim, H. (2000) · Technometrics · doi:10.1080/00401706.2000.10485722 · Cited by 5,508
Markov Chains and Stochastic Stability
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Self-citationUpdating Schemes, Correlation Structure, Blocking and Parameterization for the Gibbs Sampler
Roberts, G. O., & Sahu, S. K. (1997) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00070 · Cited by 409
Self-citationEstimation of Population Growth or Decline in Genetically Monitored Populations
Beaumont, M. A. (2003) · Genetics · doi:10.1093/genetics/164.3.1139 · Cited by 405
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
Markov chain Monte Carlo model determination for hierarchical and graphical log-linear models
Dellaportas, P. (1999) · Biometrika · doi:10.1093/biomet/86.3.615 · Cited by 163
Analyses of Infectious Disease Data from Household Outbreaks by Markov Chain Monte Carlo Methods
O’Neill, P. D., Balding, D. J., Becker, N. G., Eerola, M., & Mollison, D. (2000) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/1467-9876.00210 · Cited by 138
Cited by
Approximate Bayesian Computation in Evolution and Ecology
Beaumont, M. A. (2010) · Annual Review of Ecology, Evolution, and Systematics · doi:10.1146/annurev-ecolsys-102209-144621 · Cited by 1,114
Statistical inference for stochastic simulation models - theory and application
Hartig, F., Calabrese, J. M., Reineking, B., Wiegand, T., & Huth, A. (2011) · Ecology Letters · doi:10.1111/j.1461-0248.2011.01640.x · Cited by 384
Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy
Sharma, S. (2017) · Annual Review of Astronomy and Astrophysics · doi:10.1146/annurev-astro-082214-122339 · Cited by 298
Golightly, A., & Wilkinson, D. J. (2011) · Interface Focus · doi:10.1098/rsfs.2011.0047 · Cited by 282
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
Metadata
- Authors
- Andrieu, C., & Roberts, G. O.
- Venue
- The Annals of Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 2009-04-01
- Volume
- 37
- Issue
- 2
- Pages
- 697-725
- ISSN
- 0090-5364 (ISSN-L)
- OpenAlex
- W2091860746
- Cited by
- 687
- References
- 10
- FWCI
- 10.39
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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