REVISING BELIEFS IN NONIDENTIFIED MODELS
研究了非识别模型中的贝叶斯分析，指出使用恰当先验时数据对某些量无信息，而不恰当先验会导致后验分布不当，通过实例分析两种情形。
A Bayesian analysis of a nonidentified model is always possible if a proper prior on all the parameters is specified. There is, however, no Bayesian free lunch. The “price” is that there exist quantities about which the data are uninformative, i.e., their marginal prior and posterior distributions are identical. In the case of improper priors the analysis is problematic—resulting posteriors can be improper. This study investigates both proper and improper cases through a series of examples.