BAYESIAN NONPARAMETRICS HJORT PDF
For nonparametric Bayesian inference we use a prior which supports piecewise linear quantile functions, based on the need to work with a finite set of partitions, . Nils Lid Hjort, Chris Holmes, Peter Müller, and Stephen G. Walker the history of the still relatively young field of Bayesian nonparametrics, and offer some. Part III: Bayesian Nonparametrics. Nils Lid Hjort. Department of Mathematics, University of Oslo. Geilo Winter School, January 1/
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Hjort , Walker : Quantile pyramids for Bayesian nonparametrics
Nils Lid Hjort and Stephen G. With quantile pyramids we instead fix probabilities and use random partitions.
For nonparametric Bayesian inference we use a prior which supports piecewise linear quantile functions, based nonpaametrics the need to work with a finite set of partitions, yet we show that the limiting version of the prior exists. We also discuss and investigate an alternative model based on the so-called substitute likelihood.
Both approaches factorize in a convenient way leading to relatively straightforward analysis via MCMC, since analytic summaries of posterior distributions are too complicated. We give conditions securing the existence of an absolute continuous quantile process, and discuss consistency and approximate normality for the sequence of posterior distributions.
Permanent link to this document https: Zentralblatt MATH identifier Quantile pyramids for Bayesian nonparametrics. More by Nils Lid Hjort Search this author in: Bayesin Scholar Project Euclid. More by Stephen G.
Walker Search this author in: Article information Source Ann. Dates First available in Project Bayesin Download Email Please enter a valid email address. The consistency of posterior distributions in nonparametric problems.
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