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Kullback-Leibler Divergence

Description

This equation represents how Kullback-Leibler Divergence is calculated. It is a metric used to determine the "distance between" any given two probability distributions (here denoted by \( \htmlClass{sdt-0000000131}{X} \)probDistribution and \(\hat{\htmlClass{sdt-0000000131}{X}probDistribution}\) respectively). It can be interpreted as "the expected excess surprise from using \(\hat{\htmlClass{sdt-0000000131}{X}probDistribution}\) as a model instead of \( \htmlClass{sdt-0000000131}{X} \)probDistribution when the actual model is \( \htmlClass{sdt-0000000131}{X} \)probDistribution"

\[KL(\htmlClass{sdt-0000000131}{X}probDistribution, \hat{\htmlClass{sdt-0000000131}{X}probDistribution}) = \htmlClass{sdt-0000000080}{\sum}_{\htmlClass{sdt-0000000091}{\mathbf{s}} \in \htmlClass{sdt-0000000026}{S}} \htmlClass{sdt-0000000131}{X}probDistribution(\htmlClass{sdt-0000000091}{\mathbf{s}}) \frac{\htmlClass{sdt-0000000131}{X}log(\htmlClass{sdt-0000000131}{X}probDistribution(\htmlClass{sdt-0000000091}{\mathbf{s}}))}{\htmlClass{sdt-0000000131}{X}log(\hat{\htmlClass{sdt-0000000131}{X}probDistribution}(\htmlClass{sdt-0000000091}{\mathbf{s}}))}\]

Symbols Used:

This symbol describes the Z-Transform, a mathematical tool used in digital signal processing and control systems to analyze discrete-time signals.

\( S \)

This symbol represents all possible microstates of a multi-particle system.

\( \sum \)

This is the summation symbol in mathematics, it represents the sum of a sequence of numbers.

\( \mathbf{s} \)

This symbol represents a full description of the system taken at molecular level.

References

  1. Jaeger, H. (n.d.). Neural Networks (AI) (WBAI028-05) Lecture Notes BSc program in Artificial Intelligence. Retrieved April 27, 2024, from https://www.ai.rug.nl/minds/uploads/LN_NN_RUG.pdf
  2. Wikipedia contributors. (2024, May 17). Kullback–Leibler divergence. Wikipedia. https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence