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Team:TU Darmstadt/Modelling/Statistics
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In information theory the Kullback-Leibler-Divergence (DKL) describes and quantifies the distance between | In information theory the Kullback-Leibler-Divergence (DKL) describes and quantifies the distance between | ||
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| - | + | Here, P(i) and Q(i) denote the densities of P and Q at a position i. | |
| - | + | In our study, we use the DKL to describe the distances of the survey datasets from the human practice project. | |
| + | Therefore, we have to calculate a histogram out of the different datasets. Here, it is important to perform a constant binsize. In this approach we assume that a hypothetical distribution Q is uniformly distributed. | ||
| + | To achieve this, we grate an appropriate test data set with the random generator runif in R. | ||
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Revision as of 00:47, 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.
Here, P(i) and Q(i) denote the densities of P and Q at a position i. In our study, we use the DKL to describe the distances of the survey datasets from the human practice project. Therefore, we have to calculate a histogram out of the different datasets. Here, it is important to perform a constant binsize. In this approach we assume that a hypothetical distribution Q is uniformly distributed. To achieve this, we grate an appropriate test data set with the random generator runif in R.
