Flexible non-stationary modeling of spatial outcomes using a mixed-stationary Gaussian process.
problem Limited flexibility in non-stationary models and computational intractability.
method Developed a non-stationary Gaussian process with individually set stationarity parameters at each location, using a non-parametric mixture model to reduce parameters and incorporate spatial correlation.
result Improved prediction efficiency through spatially correlated components in the mixture model.