Carlin, B.P. and Polson, N.G. (1991). Inference for Nonconjugate Bayesian Models using the Gibbs sampler. Cand. J. Statistics, 19, 399-405. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Nonlinear and Non-Gaussian State-Space Modeling with Monte Carlo .. - Tanizaki (2000) (5 citations) (Correct)
....implies large number of A. Thus, the random number generator by rejection sampling is ine#cient when the acceptance probability #( is close to zero. That is, for rejection sampling, it sometimes takes a long time, especially when the acceptance probability #( is small. See, for example, Carlin and Polson (1991) and Carlin, Polson and Sto#er (1992) Thus, the rejection sampling has the disadvantage that we cannot exactly predict computation time. To improve the rejection sampling procedure in the sense of computation time, we have the following strategies. One is that we may pick another j and or i in ....
Carlin, B.P. and Polson, N.G., 1991, " Inference for Nonconjugate Bayesian Models Using the Gibbs Sampler, " Canadian Journal of Statistics, Vol.19, pp.399--405.
On Markov Chain Monte Carlo Methods For Nonlinear And.. - Geweke, Tanizaki (1999) (2 citations) (Correct)
....proposal density required to perform the Metropolis Hastings algorithm have been examined. APPENDICES APPENDIX 1: MARKOV CHAIN MONTE CARLO METHODS Appendix 1. 1: Gibbs Sampling Geman and Geman (1984) Tanner and Wong (1987) Gelfand, Hills, RacinePoon and Smith (1990) Gelfand and Smith (1990) Carlin and Polson (1991), Zeger and Karim (1991) and so on developed the Gibbs sampling theory. Carlin, Polson and Sto#er (1992) Carter and Kohn (1994, 1996) and De Jong and Shephard (1995) applied to the Gibbs sampler to the nonlinear and or non Gaussian state space models. The Gibbs sampling theory is concisely ....
Carlin, B.P. and Polson, N.G., 1991, " Inference for Nonconjugate Bayesian Models Using the Gibbs Sampler, " Canadian Journal of Statistics, Vol.19, pp.399 -- 405.
Conjugate Analysis of Multivariate Normal Data with.. - Dominici, Parmigiani.. (1999) (Correct)
....[23] are commonly used in Bayesian parametric inference, because of their computational convenience and relative ease of elicitation and interpretation. While recent progress in simulation methods has opened several opportunities for useful non conjugate distribution (see Carlin and Polson [5]) a significant amount of applied work still makes use of conjugate priors. For example, parameters characterizing components of complex models are often assigned conjugate priors to obtain easy to sample full conditional distributions. In missing data problems, popular approaches are based on ....
Carlin, B.P. and Polson, N.G. (1991) "Inference for nonconjugate Bayesian models using the Gibbs sampler" Canadian Journal of Statistics, 19, 399--405.
Conjugate Analysis of Multivariate Normal Data With.. - Francesca Dominici.. (Correct)
....(Rai#a Schleifer 1961) are commonly used in Bayesian parametric inference, because of their computational convenience and relative ease of elicitation and 1 interpretation. While recent progress in simulation methods has opened several opportunities for useful non conjugate distributions (cf. Carlin Polson 1991), a significant amount of applied work still makes use of conjugate priors. For example, parameters characterizing components of complex models are often assigned conjugate priors to obtain easy to sample full conditional distributions. In missing data problems, popular approaches are based on ....
B. P. Carlin & N. G. Polson (1991). Inference for nonconjugate Bayesian models using the Gibbs sampler. The Canadian Journal of Statistics, 19, 399--405.
Conjugate Analysis of Multivariate Normal Data with.. - Francesca Dominici (Correct)
....[21] are commonly used in Bayesian parametric inference, because of their computational convenience and relative ease of elicitation and interpretation. While recent progress in simulation methods has opened several opportunities for useful non conjugate distribution (see Carlin and Polson [4]) a significant amount of applied work still makes use of conjugate priors. For example, parameters characterizing components of complex models are often assigned conjugate priors to obtain easy to sample full conditional distributions. In missing data problems, popular approaches are based on ....
Carlin, B.P. and Polson, N.G. (1991) "Inference for nonconjugate Bayesian models using the Gibbs sampler" Canadian Journal of Statistics, 19, 399--405.
MCMC Methods for Financial Econometrics - Johannes, Polson (2002) Self-citation (Polson) (Correct)
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Carlin, B.P. and Polson, N.G. (1991). Inference for Nonconjugate Bayesian Models using the Gibbs sampler. Cand. J. Statistics, 19, 399-405.
Diagnostic Measures for Model Criticism - Carota, Parmigiani, Polson (1993) (6 citations) Self-citation (Polson) (Correct)
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Carlin, B.P. and Polson, N.G. (1991a). Inference for nonconjugate Bayesian models using the Gibbs sampler. Canadian Journal of Statistics 19, pp. 399--405.
Bayesian Estimation of State-Space Models Using the.. - Geweke, Tanizaki (Correct)
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Carlin, B.P. and Polson, N.G., 1991, " Inference for Nonconjugate Bayesian Models Using the Gibbs Sampler, " Canadian Journal of Statistics, Vol.19, pp.399 -- 405.
A Bayesian Approach to Testing for Markov Switching in.. - Kim, Nelson (2000) (Correct)
No context found.
Carlin, B. P., and Polson, N. G., 1991, \Inference for Nonconjugate Bayesian Models Using the Gibbs Sampler," Canadian Journal of Statistics, 19, 399-405.
A Bayesian Approach to Testing for Markov Switching in.. - Kim, Nelson (1999) (Correct)
No context found.
Carlin, B. P., and Polson, N. G., 1991, \Inference for Nonconjugate Bayesian Models Using the Gibbs Sampler," Canadian Journal of Statistics, 19, 399-405.
Is There a Structural Break in the Equity Premium? - Kim, Morley, Nelson (2000) (Correct)
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Carlin, B. P., and Polson, N. G., 1991, "Inference for Nonconjugate Bayesian Models Using the Gibbs Sampler," Canadian Journal of Statistics, 19, 399-405.
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