Recently, James [L.F. James, Bayesian Poisson process partition calculus with an application to Bayesian Lévy moving averages, Ann. Statist. 33 (2005), pp. 1771–1799.] and [L.F. James, Poisson calculus for spatial neutral to the right processes, Ann. Statist. 34 (2006), pp. 416–440.] has derived important results for various models in Bayesian nonparametric inference. In particular, in ref. [L.F. James, Poisson calculus for spatial neutral to the right processes, Ann. Statist. 34 (2006), pp. 416–440.] a spatial version of neutral to the right processes is defined and their posterior distribution derived. Moreover, in ref. [L.F. James, Bayesian Poisson process partition calculus with an application to Bayesian Lévy moving averages, Ann. Statist. 33 (2005), pp. 1771–1799.] the posterior distribution for an intensity or hazard rate modelled as a mixture under a general multiplicative intensity model is obtained. His proofs rely on the so-called Bayesian Poisson partition calculus. Here we provide alternative proofs based on a different technique.

Posterior analysis for some classes of nonparametric models

LIJOI, ANTONIO;PRUENSTER, IGOR;
2008

Abstract

Recently, James [L.F. James, Bayesian Poisson process partition calculus with an application to Bayesian Lévy moving averages, Ann. Statist. 33 (2005), pp. 1771–1799.] and [L.F. James, Poisson calculus for spatial neutral to the right processes, Ann. Statist. 34 (2006), pp. 416–440.] has derived important results for various models in Bayesian nonparametric inference. In particular, in ref. [L.F. James, Poisson calculus for spatial neutral to the right processes, Ann. Statist. 34 (2006), pp. 416–440.] a spatial version of neutral to the right processes is defined and their posterior distribution derived. Moreover, in ref. [L.F. James, Bayesian Poisson process partition calculus with an application to Bayesian Lévy moving averages, Ann. Statist. 33 (2005), pp. 1771–1799.] the posterior distribution for an intensity or hazard rate modelled as a mixture under a general multiplicative intensity model is obtained. His proofs rely on the so-called Bayesian Poisson partition calculus. Here we provide alternative proofs based on a different technique.
2008
2008
Lijoi, Antonio; Pruenster, Igor; S. G., Walker
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11565/3989917
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