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Bayesian demography 250 years after Bayes

Bayesian statistics offers an alternative to classical (frequentist) statistics. It is distinguished by its use of probability distributions to describe uncertain quantities, which leads to elegant solutions to many difficult statistical problems. Although Bayesian demography, like Bayesian statisti...

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Detalles Bibliográficos
Autores principales: Bijak, Jakub, Bryant, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Routledge 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4867874/
https://www.ncbi.nlm.nih.gov/pubmed/26902889
http://dx.doi.org/10.1080/00324728.2015.1122826
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author Bijak, Jakub
Bryant, John
author_facet Bijak, Jakub
Bryant, John
author_sort Bijak, Jakub
collection PubMed
description Bayesian statistics offers an alternative to classical (frequentist) statistics. It is distinguished by its use of probability distributions to describe uncertain quantities, which leads to elegant solutions to many difficult statistical problems. Although Bayesian demography, like Bayesian statistics more generally, is around 250 years old, only recently has it begun to flourish. The aim of this paper is to review the achievements of Bayesian demography, address some misconceptions, and make the case for wider use of Bayesian methods in population studies. We focus on three applications: demographic forecasts, limited data, and highly structured or complex models. The key advantages of Bayesian methods are the ability to integrate information from multiple sources and to describe uncertainty coherently. Bayesian methods also allow for including additional (prior) information next to the data sample. As such, Bayesian approaches are complementary to many traditional methods, which can be productively re-expressed in Bayesian terms.
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spelling pubmed-48678742016-05-23 Bayesian demography 250 years after Bayes Bijak, Jakub Bryant, John Popul Stud (Camb) Review Article Bayesian statistics offers an alternative to classical (frequentist) statistics. It is distinguished by its use of probability distributions to describe uncertain quantities, which leads to elegant solutions to many difficult statistical problems. Although Bayesian demography, like Bayesian statistics more generally, is around 250 years old, only recently has it begun to flourish. The aim of this paper is to review the achievements of Bayesian demography, address some misconceptions, and make the case for wider use of Bayesian methods in population studies. We focus on three applications: demographic forecasts, limited data, and highly structured or complex models. The key advantages of Bayesian methods are the ability to integrate information from multiple sources and to describe uncertainty coherently. Bayesian methods also allow for including additional (prior) information next to the data sample. As such, Bayesian approaches are complementary to many traditional methods, which can be productively re-expressed in Bayesian terms. Routledge 2016-01-02 2016-02-23 /pmc/articles/PMC4867874/ /pubmed/26902889 http://dx.doi.org/10.1080/00324728.2015.1122826 Text en © 2016 The Author(s). Published by Taylor & Francis. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review Article
Bijak, Jakub
Bryant, John
Bayesian demography 250 years after Bayes
title Bayesian demography 250 years after Bayes
title_full Bayesian demography 250 years after Bayes
title_fullStr Bayesian demography 250 years after Bayes
title_full_unstemmed Bayesian demography 250 years after Bayes
title_short Bayesian demography 250 years after Bayes
title_sort bayesian demography 250 years after bayes
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4867874/
https://www.ncbi.nlm.nih.gov/pubmed/26902889
http://dx.doi.org/10.1080/00324728.2015.1122826
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