Bayesian demography 250 years after Bayes
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, what 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. In general, the ability to integrate information from multiple sources, and to describe uncertainty coherently, is the key advantage of Bayesian methods. 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.
Bayesian demography, Bayesian statistics, demographic methodology, population estimates and forecasts, statistical methods
1-19
Bijak, Jakub
e33bf9d3-fca6-405f-844c-4b2decf93c66
Bryant, John
d18a9c25-73bf-4172-b987-3378da9ba775
23 February 2016
Bijak, Jakub
e33bf9d3-fca6-405f-844c-4b2decf93c66
Bryant, John
d18a9c25-73bf-4172-b987-3378da9ba775
Abstract
Bayesian statistics offers an alternative to classical (frequentist) statistics. It is distinguished by its use of probability distributions to describe uncertain quantities, what 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. In general, the ability to integrate information from multiple sources, and to describe uncertainty coherently, is the key advantage of Bayesian methods. 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.
Text
Bijak_Bryant_2015_Population_Studies.pdf
- Accepted Manuscript
More information
Accepted/In Press date: 30 July 2015
Published date: 23 February 2016
Keywords:
Bayesian demography, Bayesian statistics, demographic methodology, population estimates and forecasts, statistical methods
Organisations:
Social Statistics & Demography, Statistical Sciences Research Institute
Identifiers
Local EPrints ID: 379877
URI: http://eprints.soton.ac.uk/id/eprint/379877
ISSN: 0032-4728
PURE UUID: ecb5cc01-2ec7-44c4-aab7-4115c02dba10
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Date deposited: 24 Aug 2015 12:09
Last modified: 15 Mar 2024 05:20
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Author:
John Bryant
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