Parameter Updating in a Bayes Network
研究了将贝叶斯网络作为计算工具来更新动态线性模型中的参数估计，展示了网络如何统一刻画模型及其计算，并允许非顺序数据收集以纳入延迟数据，同时提供了在线诊断和后验分布近似方法。
Abstract A Bayes network is a directed acyclic graph in which the links are quantified by fixed conditional probabilities and the nodes represent random variables. The primary use of the network is to provide an efficient method for updating conditional probabilities in the graph. We consider the consequences of using the network as the computational device for updating parameter estimates in the dynamic linear model, a discrete time Bayesian model. We show that using the network characterizes the dynamic linear model and its computations in a unified way. The generality of the network permits nonsequential data collection and thereby provides a straightforward method of incorporating delayed data. An on-line diagnostic is offered to complement the conventional forecast error and an approximation to the posterior distribution is proposed. Algorithms for data propagation in multivariate Gaussian causal trees are presented. Key Words: Data propagationDynamic linear modelGraphical representation