Kullback Leibler Divergence Calculator





Kullback Leibler Divergence Calculator Description


Explanation

The Kullback-Leibler divergence, often known as the KL divergence, is a commonly used statistic in the literature on data mining to assess the differences between two probability distributions over the same variable x. Information theory and probability theory are where the idea first emerged.
OR
Kullback-Leibler divergence (also known as: discrimination information, information divergence, information gain, relative entropy, KLIC, KL divergence) A measure of the difference between two probability distributions P and Q.
Condition: Let p(x) and q(x) are two probability distributions of a discrete random variable x. That is, both p(x) and q(x) sum up to 1, and p(x) > 0 and q(x) > 0 for any x in X


Kullback Leibler Calculator Formula:

Kullback Leibler formula

Example Input X: 0.1, 0.2, 0.7 = These number sum must be upto 1 means 0.1 + 0.2 + 0.7 =1

Example Input Y: 0.2, 0.3, 0.5 = These number sum must be upto 1 means 0.2 + 0.3 + 0.5 = 1

Values must be in between 0 and 1

How to use Calculate Kullback Leibler Divergence using Calculator?

  • You need to enter the value of X - Probabilities in the first input field given in the form. Remember each probability is seperated with comma and each value must be in between 0 and 1.
  • Then enter the value of Y - Probabilities in the second input field given in the form. Remember each probability is seperated with comma and each value must be in between 0 and 1.
  • Click on the button submit, and the results will automatically appear below in the result section.

More Calculation Examples (Kullback Leibler Divergence Examples)

  • Example 1
  • Probabilities - X : 0.1, 0.3, 0.6
  • Probabilities - Y : 0.1, 0.2, 0.7
  • Result : 0.029149124536094

  • Example 2
  • Probabilities - X : 0.2, 0.3, 0.5
  • Probabilities - Y : 0.6, 0.3, 0.1
  • Result : 0.58499649848343

  • Example 3
  • Probabilities - X : 0.5, 0.2, 0.3
  • Probabilities - Y : 0.2, 0.3, 0.5
  • Result : 0.22380465718565

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  • How to Calculate the KL Divergence for Machine Learning
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