# 쿨백-라이블러 발산

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## 노트

• One important thing to note is that the KL Divergence is an asymmetric measure (i.e. KL(P,Q) ![1]
• As expected we see a smaller KL Divergence for distributions 1 & 2 than 1 & 3.[1]
• And we also see the KL Divergence of a distribution with itself is 0.[1]
• Finally, we comment on recent applications of KL divergence in the neural coding literature and highlight its natural application.[2]
• Proposition Let and be two probability density functions such that their KL divergence is well-defined.[3]
• This study also investigates a variety of applications of KL divergence in medical diagnostics.[4]
• Graphically, KL divergence depicted through the information graph.[4]
• It described an application of the KL divergence for discrete biomarkers.[4]
• Section 2 describes preliminaries, including mathematical details of the KL divergence.[4]
• Optimal encoding of information is a very interesting topic, but not necessary for understanding KL divergence.[5]
• With KL divergence we can calculate exactly how much information is lost when we approximate one distribution with another.[5]
• Now we can go ahead and calculate the KL divergence for our two approximating distributions.[5]
• We can double check our work by looking at the way KL Divergence changes as we change our values for this parameter.[5]
• It is a great post explaining the KL divergence, but felt some of the intricacies in the explanation can be explained in more detail.[6]
• Let us now compute the KL divergence for each of the approximate distributions we came up with.[6]
• First we will see how the KL divergence changes when the success probability of the binomial distribution changes.[6]
• You can see that as we are moving away from our choice (red dot), the KL divergence rapidly increases.[6]
• The SciPy library provides the kl_div() function for calculating the KL divergence, although with a different definition as defined here.[7]
• It also provides the rel_entr() function for calculating the relative entropy, which matches the definition of KL divergence here.[7]
• # example of calculating the kl divergence (relative entropy) with scipy from scipy .[7]
• It uses the KL divergence to calculate a normalized score that is symmetrical.[7]
• Relative entropy relates to " rate function " in the theory of large deviations .[8]
• Relative entropy remains well-defined for continuous distributions, and furthermore is invariant under parameter transformations .[8]
• Relative entropy is directly related to the Fisher information metric .[8]

## 메타데이터

### Spacy 패턴 목록

• [{'LOWER': 'kullback'}, {'OP': '*'}, {'LOWER': 'leibler'}, {'LEMMA': 'divergence'}]
• [{'LOWER': 'information'}, {'LEMMA': 'divergence'}]
• [{'LOWER': 'information'}, {'LEMMA': 'gain'}]
• [{'LOWER': 'relative'}, {'LEMMA': 'entropy'}]
• [{'LOWER': 'kl'}, {'LEMMA': 'divergence'}]
• [{'LEMMA': 'KLIC'}]