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Random Field Simulation

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Random Field Simulation

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14 May 2010 (Updated )

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

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Description

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.

Acknowledgements

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

05 Nov 2013 nacim Debabi

nice trick

26 Apr 2012 Paul Constantine

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

12 Apr 2012 Songhun  
16 Sep 2011 João  
01 Apr 2011 Felipe G. Nievinski  
Updates
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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