In time series analysis, continuous time ARMA models can be used to fit models to unequally spaced observations. These continuous time ARMA models are based on stochastic differential equations.
Similar results are available for space-time processes.
For example, the stochastic diffusion or heat conduction equation is one possible model for space-time processes. These methods provide an alternative to the usual Kriging methods. This talk will discuss fitting these models to unequally spaced spatial or space time observations. For Gaussian data, maximum likelihood or approximate maximum likelihood based on nearest neighbors will be used. Often the purpose of the analysis is to predict the value of the field on a grid so contour maps can be drawn.