Sampling truncated normal, beta, and gamma densities

Journal of Computational and Graphical Statistics

Volumn 10, Issue 2, 2001, Pages 206-215

  Damien, Paul ^a   Walker, Stephen G ^b  

^a UNIVERSITY OF MICHIGAN   (United States)

^b UNIVERSITY OF BATH   (United Kingdom)

Author keywords

Gibbs sampler; Latent variables; Uniform random variables

Indexed keywords

EID: 0035615238     PISSN: 10618600     EISSN: 15372715     Source Type: Journal    
DOI: 10.1198/10618600152627906     Document Type: Article

References (12)

1. Damien, P., Wakefield, J. C., and Walker, S. G. (1999), “Gibbs Sampling for Bayesian Non-conjugate and Hierar chical Models Using Auxiliary Variables,” revised for Journal of the RoyalStatisticalSociety, Series B, 61, 331-344.
2. Devroye, L. (1986), Non-uniformRandom Variate Generation, New York: Springer-Verlag.
3. Gelfand, A. E., Smith, A. F. M., andLee, T. M. (1992), “Bayesian AnalysisofConstrainedParameter andTruncated Data Problems Using Gibbs Sampling,” Journal of the American Statistical association, 87, 523-532.
4. Gilks, W. R., and Wild, P. (1992), “Adaptive Rejection Sampling for Gibbs Sampling,” Applied Statistics, 41, 337-348.
5. Higdon, D. (1998), “Auxiliary Variable Methods for Markov Chain Monte Carlo Methods With Applications,” Journal of the American Statistical association, 93, 585-595.
6. Laud, P. W., Smith, A. F. M., and Damien, P. (1996), “MonteCarlo Methodsfor ApproximatingaPosteriorHazard Rate Process,” Statistics and Computing, 6, 77-84.
7. Polson, N. G. (1996), “Convergence of Markov Chain Monte Carlo Algorithms,” in Bayesian Statistics 5, eds. J. M. Bernardo, J. O. Berger, A. P. Dawid, and A. F. M. Smith, Oxford: Oxford University Press, pp. 297-321.
8. Robert, C. P. (1995), “Simulation of Truncated Normal Variables,” Statistics and Computing, 5, 121-125.
9. Smith, A. F. M., and Roberts, G. O. (1993), “Bayesian Computationviathe Gibbs Sampler and Related Markov Chain Monte Carlo Methods,” Journal of the RoyalStatisticalSociety, Series B, 55, 3-24.
10. Swendsen, R. H., and Wang, J. S. (1987), “Nonuniversal Critical Dynamics in Monte Carlo Simulations,” Physical Review Letters, 58, 86-88.
11. Tierney, L. (1994), “Markov Chains for Exploring Posterior Distributions” (with discussion), The Annals of Statistics, 22, 1701-1762.
12. Wakefield, J. C., Smith, A. F. M., Racine-Poon, A., and Gelfand, A. E. (1994), “Bayesian Analysis of Linear and Nonlinear Population Models Using the Gibbs Sampler,” AppliedStatistics, 43, 201-221