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|Title:||On Bayesian nonparametric modelling of two correlated distributions||Authors:||Kolossiatis, Michalis
Griffin, Jim E.
Steel, Mark F J
|Keywords:||Dependent Dirichlet process;Markov chain Monte Carlo;Normalised random measures;Pólya-urn scheme;Split-merge move||Category:||Mathematics||Field:||Natural Sciences||Issue Date:||1-Jan-2013||Publisher:||Springer Verlag||Source:||Statistics and Computing Volume 23, Issue 1, 2013, Pages 1-15||metadata.dc.doi:||10.1007/s11222-011-9283-7||Abstract:||In this paper, we consider the problem of modelling a pair of related distributions using Bayesian nonparametric methods. A representation of the distributions as weighted sums of distributions is derived through normalisation. This allows us to define several classes of nonparametric priors. The properties of these distributions are explored and efficient Markov chain Monte Carlo methods are developed. The methodology is illustrated on simulated data and an example concerning hospital efficiency measurement.||URI:||http://ktisis.cut.ac.cy/handle/10488/9928||ISSN:||09603174||Rights:||© 2011 Springer Science+Business Media, LLC.||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
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