relative entropy
Let and be probability distributions with supports and respectively, where . The relative entropy or Kullback-Leibler distance between two probability distributions and is defined as
(1) |
While is often called a distance, it is not a true metric because it is not symmetric and does not satisfy the triangle inequality. However, we do have with equality iff .
(2) | ||||
(3) | ||||
(4) | ||||
(5) | ||||
(6) | ||||
(7) |
where the first inequality follows from the concavity of and the second from expanding the sum over the support of rather than .
Relative entropy also comes in a continuous version which looks just as one might expect. For continuous distributions and , the support of , we have
(8) |
Title | relative entropy |
Canonical name | RelativeEntropy |
Date of creation | 2013-03-22 12:19:32 |
Last modified on | 2013-03-22 12:19:32 |
Owner | Mathprof (13753) |
Last modified by | Mathprof (13753) |
Numerical id | 10 |
Author | Mathprof (13753) |
Entry type | Definition |
Classification | msc 60E05 |
Classification | msc 94A17 |
Synonym | Kullback-Leibler distance |
Related topic | Metric |
Related topic | ConditionalEntropy |
Related topic | MutualInformation |
Related topic | ProofOfGaussianMaximizesEntropyForGivenCovariance |