Bijak Jakub, Bryant John
a University of Southampton.
b Statistics New Zealand.
Popul Stud (Camb). 2016;70(1):1-19. doi: 10.1080/00324728.2015.1122826. Epub 2016 Feb 23.
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.
贝叶斯统计为经典（频率论）统计提供了一种替代方法。它的特点是使用概率分布来描述不确定量，这为许多困难的统计问题带来了优雅的解决方案。尽管贝叶斯人口统计学与更广义的贝叶斯统计一样，大约有250年的历史，但直到最近才开始蓬勃发展。本文的目的是回顾贝叶斯人口统计学的成就，解决一些误解，并论证在人口研究中更广泛地使用贝叶斯方法的理由。我们重点关注三个应用领域：人口预测、有限数据以及高度结构化或复杂的模型。贝叶斯方法的关键优势在于能够整合来自多个来源的信息，并连贯地描述不确定性。贝叶斯方法还允许在数据样本之外纳入额外的（先验）信息。因此，贝叶斯方法与许多传统方法相辅相成，许多传统方法可以用贝叶斯术语进行有效的重新表述。
