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Autores principales: Griffiths, Robert C., Maller, Ross A., Shemehsavar, Soudabeh
Formato: Preprint
Publicado: 2024
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Acceso en línea:https://arxiv.org/abs/2402.11563
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author Griffiths, Robert C.
Maller, Ross A.
Shemehsavar, Soudabeh
author_facet Griffiths, Robert C.
Maller, Ross A.
Shemehsavar, Soudabeh
contents A Bayesian nonparametric method of James, Lijoi \& Prunster (2009) used to predict future values of observations from normalized random measures with independent increments is modified to a class of models based on negative binomial processes for which the increments are not independent, but are independent conditional on an underlying gamma variable. Like in James et al., the new algorithm is formulated in terms of two variables, one a function of the past observations, and the other an updating by means of a new observation. We outline an application of the procedure to population genetics, for the construction of realisations of genealogical trees and coalescents from samples of alleles.
format Preprint
id arxiv_https___arxiv_org_abs_2402_11563
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle A Gibbs Sampling Scheme for a Generalised Poisson-Kingman Class
Griffiths, Robert C.
Maller, Ross A.
Shemehsavar, Soudabeh
Methodology
A Bayesian nonparametric method of James, Lijoi \& Prunster (2009) used to predict future values of observations from normalized random measures with independent increments is modified to a class of models based on negative binomial processes for which the increments are not independent, but are independent conditional on an underlying gamma variable. Like in James et al., the new algorithm is formulated in terms of two variables, one a function of the past observations, and the other an updating by means of a new observation. We outline an application of the procedure to population genetics, for the construction of realisations of genealogical trees and coalescents from samples of alleles.
title A Gibbs Sampling Scheme for a Generalised Poisson-Kingman Class
topic Methodology
url https://arxiv.org/abs/2402.11563