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.