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Team:TU Darmstadt/Modelling/Statistics
From 2013.igem.org
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| - | + | The DKL Analysis | |
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| + | In information theory the Kullback-Leibler-Divergence (DKL) describes and quantifies the distance between | ||
| + | two distributions P and Q. Where P denotes an experimental distribution, it is compared with Q, a reference distribution. DKL is also known as ‘relative entropy’ as well as ‘mutual information’. | ||
| + | Although DKL is often used as a metric or distance measurement, it is not a true measurement because it is not symmetric. | ||
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Revision as of 00:43, 5 October 2013
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Information Theory
The DKL Analysis In information theory the Kullback-Leibler-Divergence (DKL) describes and quantifies the distance between two distributions P and Q. Where P denotes an experimental distribution, it is compared with Q, a reference distribution. DKL is also known as ‘relative entropy’ as well as ‘mutual information’. Although DKL is often used as a metric or distance measurement, it is not a true measurement because it is not symmetric.
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