Bayesian Forecasting of Multinomial Time Series through Conditionally Gaussian Dynamic Models
通过条件高斯动态模型进行多项时间序列的贝叶斯预测
We consider inference in the class of conditionally Gaussian dynamic models for nonnormal multivariate time series. In such models, data are represented as drawn from nonnormal sampling distributions whose parameters are related both through time and hierarchically across several multivariate series. A key example—the main focus here—is time series of multinomial observations, a common occurrence in sociological and demographic studies involving categorical count data.…