Uncertainty Propagation in Long-Term Structured Regression on Evolving Networks

Conditional probabilistic graphical models provide a powerful Thus, a particular interest of this paper is long-term forecasting framework for structured regression in spatiotemporal on non-static networks with continuous target variables datasets with complex correlation patterns. It has been (structured regression) and proper uncertainty propagation shown that models utilizing underlying correlation patterns estimate in such evolving networks. This is motivated (structured models) can significantly improve predictive accuracy by climate modeling of long-term precipitation prediction in as compared to models not utilizing such information spatiotemporal weather station networks, as well as prediction (Radosavljevic, Vucetic, and Obradovic 2010; 2014; of different disease trends in temporal disease-disease Ristovski et al. 2013; Wytock and Kolter 2013; Stojanovic networks.