Bayesian Inferences and Forecasting in Spatial Time Series Models

The spatial time series data can be viewed as a set of time series collected simultaneously at a number of spatial locations with time. For example, The Mumps data have a feature to infect adjacent broader regions in accordance with spatial location and time. Therefore, The spatial time series models have many parameters of space and time. In this paper, We propose the method of bayesian inferences and prediction in spatial time series models with a Gibbs Sampler in order to overcome convergence problem in numerical methods. Our results are illustrated by using the data set of mumps cases reported from the Korea Center for Disease Control and Prevention monthly over the years 2001-2009, as well as a simulation study.