The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Gibbs Sampling for Bayesian Non-Conjugate and Hierarchical Models by Using Auxiliary Variables
Abstract
Summary: We demonstrate the use of auxiliary (or latent) variables for sampling non-standard densities which arise in the context of the Bayesian analysis of non-conjugate and hierarchical models by using a Gibbs sampler. Their strategic use can result in a Gibbs sampler having easily sampled full conditionals. We propose such a procedure to simplify or speed up the Markov chain Monte Carlo algorithm. The strength of this approach lies in its generality and its ease of implementation. The aim of the paper, therefore, is to provide an alternative sampling algorithm to rejection-based methods and other sampling approaches such as the Metropolis–Hastings algorithm.
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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
Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods
Smith, A. F. M., & Roberts, G. O. (1993) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1993.tb01466.x · Cited by 1,713
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
Cited by
brms : An R Package for Bayesian Multilevel Models Using Stan
Bürkner, P.-C. (2017) · Journal of Statistical Software · doi:10.18637/jss.v080.i01 · Cited by 9,476
“…are primarily using combinations of Metropolis-Hastings updates (Metropolis, Rosenbluth, Rosenbluth, Teller, and Teller 1953; Hastings 1970) and Gibbs-sampling (Geman and Ge-man 1984; Gelfand and Smith 1990), sometimes also coupled with slice-sampling (Damien, WakeĄeld, and Walker 1999; Neal 2003).”
MethodMCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmmR Package
Hadfield, J. D. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i02 · Cited by 4,799
An introduction to MCMC for machine learning.
Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I. (2003) · Machine Learning · doi:10.1023/a:1020281327116 · Cited by 2,451
Estimating Risk Preferences from Deductible Choice
Cohen, A., & Einav, L. (2007) · American Economic Review · doi:10.1257/aer.97.3.745 · Cited by 708
Bayesian Methods for Hidden Markov Models: Recursive Computing in the 21st Century
Scott, S. L. (2002) · Journal of the American Statistical Association · doi:10.1198/016214502753479464 · Cited by 517
Metadata
- Authors
- Damlen, P., Wakefield, J., & Walker, S.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 1999-07-01
- Volume
- 61
- Issue
- 2
- Pages
- 331-344
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2128981260
- Cited by
- 361
- References
- 3
- FWCI
- 13.87
- Type
- journal article
- Language
- en
- Open access
- closed
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- Damlen, P., Wakefield, J., & Walker, S. (1999). Gibbs Sampling for Bayesian Non-Conjugate and Hierarchical Models by Using Auxiliary Variables. Journal of the Royal Statistical Society Series B: Statistical Methodology. https://doi.org/10.1111/1467-9868.00179
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