Given a list of d-dimensional points -- typically, though not necessarily, representing a mesh -- and correlation information, the function randomfield.m returns realizations of a corresponding random process. These fields may be conditioned on known data values.
The correlation information can be:
- one of three parameterized models,
- a given correlation matrix with dimensions corresponding to the number of mesh points,
- a matrix of "snapshots" of an unknown process.
The function can also return a struct with the Karhunen-Loeve bases for further field generation and filtering. See the options described in the help for more details.
When data is given for the field realizations to interpolate, the returned mean is the ordinary kriging approximation.
If you have the parallel computing toolbox and more than one core, this will go faster.
Copyright Paul G. Constantine and Qiqi Wang.