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2016 ; 70
(1
): 1-19
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Bayesian demography 250?years after Bayes
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Bijak J
; Bryant J
Popul Stud (Camb)
2016[]; 70
(1
): 1-19
PMID26902889
show ga
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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