Description: The Markov random field is generally discrete. Of course there are continuous Markov random field, assuming that the Markov random field defined in a domain of U and above any X in U, Y (x) are subject to Gauss distribution, and between the two covariance function K (x, y) =G (x, y), where G is the corresponding Green function U, then this is a random field Markov random field, but also with the Gauss airport.
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File list (Check if you may need any files):
| Filename | Size | Date |
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| GMRF | 0 | 2017-07-27
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| GMRF\GMRF | 0 | 2018-01-18
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| GMRF\GMRF\Gmrf_12.m | 190 | 2018-01-18
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| GMRF\GMRF\TestGMRF.m | 312 | 2017-07-27
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| GMRF\GMRF\X_GmrfPara_2Order_Estimat.m | 956 | 2015-10-26
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| GMRF\GMRF\X_GmrfPara_4Order_Estimat.m | 948 | 2015-10-26
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| GMRF\GMRF\X_GmrfPara_5Order_Estimat.m | 965 | 2017-07-27
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| GMRF\GMRF\X_Gmrf_ParaG.m | 2114 | 2015-10-26
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| GMRF\GMRF\main_GMRF.m | 1587 | 2017-10-18
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| GMRF\GMRF\texture.jpg | 111945 | 2009-03-16
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| GMRF\MRFFeature.rar | 104006 | 2015-10-26
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| GMRF\texture.jpg | 111945 | 2009-03-16 |
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