Bayesian Forecasting Using Spatio-temporal Models with Applications to Ozone Concentration Levels in the Eastern United States

Bayesian forecasting in time and interpolation in space is a challenging task due to the complex nature of spatio-temporal dependencies that need to be modeled for better understanding and description of the underlying processes. The problem exacerbates further when the geographical study region, such as the one in the Eastern United States considered in this chapter, is vast and the training data set for forecasting, and modelling, is rich in both space and time. This chapter develops forecasting methods for three recently