Bayesian Asymptotic Theory in a Time Series Model with a Possible Nonstationary Process
研究了在可能非平稳的时间序列模型中，贝叶斯后验的渐近正态性成立的条件，对使用贝叶斯方法处理非平稳数据的学者有参考价值。
Asymptotic normality of the Bayesian posterior is a well-known result for stationary dynamic models or nondynamic models. This paper extends the analysis to a time series model with a possible nonstationary process. We spell out conditions under which asymptotic normality of the posterior is obtained even if the true data-generation process is a nonstationary process.