Abstract

We develop a probability forecasting model through a synthesis of Bayesian beliefnetwork models and classical time-series analysis. By casting Bayesian time-series analyses as temporal belief-network problems, weintroduce dependency models that capture richer and more realistic models of dynamic dependencies. With richer models and associated computational methods, we can movebeyond the rigid classical assumptions of linearityin the relationships among variables and of normality of their probability distributions.

Citations