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Posterior analysis for some classes of nonparametric models

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Author Info
Antonio Lijoi ()
Igor Pruenster ()
Stephen G. Walker ()
Abstract

Recently, James [15, 16] has derived important results for various models in Bayesian nonparametric inference. In particular, he de ned a spatial version of neutral to the right processes and derived their posterior distribution. Moreover, he obtained the posterior distribution for an intensity or hazard rate modeled as a mixture under a general multiplicative intensity model. His proofs rely on the so{called Bayesian Poisson partition calculus. Here we provide new proofs based on an alternative technique.

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Paper provided by ICER - International Centre for Economic Research in its series ICER Working Papers - Applied Mathematics Series with number 05-2008.

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Length: 13 pages
Date of creation: Jun 2008
Date of revision:
Handle: RePEc:icr:wpmath:05-2008

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Related research
Keywords: Bayesian Nonparametrics; Completely random measure; Hazard rate; Neutral to the right prior; Multiplicative intensity model.;

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  1. Ishwaran, Hemant & James, Lancelot F., 2004. "Computational Methods for Multiplicative Intensity Models Using Weighted Gamma Processes: Proportional Hazards, Marked Point Processes, and Panel Count Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 175-190, January. [Downloadable!] (restricted)
  2. James, Lancelot F., 2003. "A simple proof of the almost sure discreteness of a class of random measures," Statistics & Probability Letters, Elsevier, vol. 65(4), pages 363-368, December. [Downloadable!] (restricted)
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