Non parametric mixture priors based on an exponential random scheme
| Autor: | Piero Veronese, Sonia Petrone |
|---|---|
| Rok vydání: | 2002 |
| Předmět: |
Statistics and Probability
Mathematical optimization Inverse-chi-squared distribution Nonparametric statistics Bernstein polynomials Density estimation Mixture model Bayesian inference Exponential function Non-parametric Bayesian inference Hierarchical models Prior probability Applied mathematics Feller operators Mixture models Bernstein Polynomials density estimation Feller operators Hierarchical models Mixture Models Non-parametric Bayesian Inference Statistics Probability and Uncertainty Natural exponential family Mathematics |
| Zdroj: | Scopus-Elsevier |
| ISSN: | 1613-981X 1618-2510 |
| DOI: | 10.1007/bf02511443 |
| Popis: | We propose a general procedure for constructing nonparametric priors for Bayesian inference. Under very general assumptions, the proposed prior selects absolutely continuous distribution functions, hence it can be useful with continuous data. We use the notion ofFeller-type approximation, with a random scheme based on the natural exponential family, in order to construct a large class of distribution functions. We show how one can assign a probability to such a class and discuss the main properties of the proposed prior, namedFeller prior. Feller priors are related to mixture models with unknown number of components or, more generally, to mixtures with unknown weight distribution. Two illustrations relative to the estimation of a density and of a mixing distribution are carried out with respect to well known data-set in order to evaluate the performance of our procedure. Computations are performed using a modified version of an MCMC algorithm which is briefly described. |
| Databáze: | OpenAIRE |
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