PRIOR ELICITATION IN MULTIPLE CHANGE‐POINT MODELS*
讨论贝叶斯变点模型中的先验设定问题，指出均匀先验和信息分层先验的缺陷，提出一种新的均匀先验，允许部分变点发生在样本外，将变点数量视为未知，并通过人工和真实数据展示不同先验对估计和预测的显著影响。
This article discusses Bayesian inference in change‐point models. The main existing approaches treat all change‐points equally, a priori, using either a Uniform prior or an informative hierarchical prior. Both approaches assume a known number of change‐points. Some undesirable properties of these approaches are discussed. We develop a new Uniform prior that allows some of the change‐points to occur out of sample. This prior has desirable properties, can be interpreted as “noninformative,” and treats the number of change‐points as unknown. Artificial and real data exercises show how these different priors can have a substantial impact on estimation and prediction.