Gelfand, A. E. and Kottas, A. 2002. A computational approach for full nonparametric Bayesian inference under Dirichlet process mixture models. Journal of Computational and Graphical Statistics, 11:289--305. Home/Search Document Details and Download
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Modeling Variability Order: A Semiparametric Bayesian Approach - Kottas, Gelfand (2000) Self-citation (Gelfand Kottas) (Correct)
....a natural and exible de nition based upon a sign changes condition. Under this de nition we develop semiparametric Dirichlet process mixture models for variability ordering, extending the ideas in Gelfand and Kottas (2000) Full comparative inference can be implemented employing the approach in Gelfand and Kottas (1999). Dirichlet process mixing provides a rich and computationally feasible framework for Bayesian nonparametric inference. We use it as the stochastic mechanism under which variability order is modeled. The format of the paper is as follows. A brief review of Dirichlet process mixing and inference ....
....(1995) describe how to use these samples to infer about linear functionals associated with F ( G) They show how posterior expectations of linear functionals and products of linear functionals can be computed. Restriction to posterior moments of linear functionals severely limits inference. Gelfand and Kottas (1999) provide a computational approach to obtain the entire posterior distribution for more general functionals. Brie y, note that for a linear functional H, H(F ( G) R H(F ( 0 ) G(d 0 ) Now, instead of marginalizing over G in [ 0 ; G j D] D j ] 0 ; j G] G] observe that this ....
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Gelfand, A.E., and Kottas, A. (1999), \A Computational Approach for Full Nonparametric Bayesian Inference in Single and Multiple Sample Problems," Technical Report 99-08, University of Connecticut, Department of Statistics.
Nonparametric empirical Bayes for the Dirichlet process.. - Jon Mcauliffe David (Correct)
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Gelfand, A. E. and Kottas, A. 2002. A computational approach for full nonparametric Bayesian inference under Dirichlet process mixture models. Journal of Computational and Graphical Statistics, 11:289--305.
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