UNCERTAIN REASONING AND FORECASTING

We develop a probability forecasting model through a synthesis of Bayesian belief-network models and classical time-series analysis. By casting Bayesian time-series analyses as temporal belief-network problems, we introduce dependency models that capture richer and more realistic models of dynamic dependencies. With richer models and associated computational methods, we can move beyond the rigid classical assumptions of linearity in the relationships among variables and of normality of their probability distributions. We apply the methodology to the difficult problem of predicting outcome in critically ill patients. The nonlinear, dynamic behavior of the critical-care domain highlights the need for a synthesis of probability forecasting and uncertain reasoning.