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Random Field Simulation

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Random Field Simulation


Paul Constantine


14 May 2010 (Updated )

Generate multivariate conditional random fields given a mesh and covariance information.

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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.


This file inspired Pm Pack Parameterized Matrix Package.

MATLAB release MATLAB 7.13 (R2011b)
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Comments and Ratings (6)
01 Oct 2014 JINGLAI


Thanks very much

Comment only
05 Nov 2013 nacim Debabi

nacim Debabi

nice trick

Comment only
26 Apr 2012 Paul Constantine

Paul Constantine

Just fixed a few bugs. It should respect the data points now.

Comment only
12 Apr 2012 Songhun


16 Sep 2011 João


01 Apr 2011 Felipe G. Nievinski

Felipe G. Nievinski

14 May 2010

Fixed a bug when computing the covariance matrix from snapshots.

05 Dec 2011

Latest version incorporates a low-memory option for large meshes. However, it is slow.

Performance is substantially improved when using the Parallel Computing Toolbox. The scripts use parfor to construct the correlation matrix.

26 Apr 2012

Learned to use zip.

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