Jensen–Shannon Divergence
The Jensen–Shannon divergence is a statistical measure used to quantify the similarity between two probability distributions. It is a symmetrized and smoothed version of the Kullback–Leibler divergence, ensuring finite values and symmetry. This measure is particularly useful in various fields, including information theory and statistics.
- ▪The Jensen–Shannon divergence is named after Johan Jensen and Claude Shannon.
- ▪It is based on the Kullback–Leibler divergence but has notable differences, such as being symmetric.
- ▪The divergence is bounded between 0 and 1 for two discrete probability distributions.
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Toggle the table of contents Jensen–Shannon divergence 4 languages CatalàPolskiРусскийTiếng Việt Edit links ArticleTalk English ReadEditView history Tools Tools move to sidebar hide Actions Read Edit View history General What links hereRelated changesUpload filePermanent linkPage informationCite this pageGet shortened URL Print/export Download as PDFPrintable version In other projects Wikidata item Appearance move to sidebar hide From Wikipedia, the free encyclopedia Statistical distance measure In probability theory and statistics, the Jensen–Shannon divergence, named after Johan Jensen and Claude Shannon, is a method of measuring the similarity between two probability distributions.
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