 Bayesian demography 250 years after BayesJakub Bijak and John Bryant Population Studies, 2016, vol. 70, issue 1, 1-19 Abstract: 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. Date: 2016 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:rpstxx:v:70:y:2016:i:1:p:1-19 Ordering information: This journal article can be ordered from DOI: 10.1080/00324728.2015.1122826 Access Statistics for this article Population Studies is currently edited by John Simons, Francesco Billari, James J. Brown, John Cleland, Andrew Foster, John McDonald, Tom Moultrie, Mikko Myrsklä, Alice Reid, Wendy Sigle-Rushton, Ronald Skeldon and Frans Willekens More articles in Population Studies from Taylor & Francis Journals |
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