Jensen–Shannon divergence

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In probability theory and statistics, the JensenShannon divergence is a popular method of measuring the similarity between two probability distributions. It is also known as information radius (IRad)[1] or total divergence to the average.[2] It is based on the Kullback–Leibler divergence, with some notable (and useful) differences, including that it is symmetric and it is always a finite value. The square root of the Jensen–Shannon divergence is a metric often referred to as Jensen-Shannon distance.[3][4]


Consider the set M_+^1(A) of probability distributions where A is a set provided with some σ-algebra of measurable subsets. In particular we can take A to be a finite or countable set with all subsets being measurable.

The Jensen–Shannon divergence (JSD) M_+^1(A) \times M_+^1(A) \rightarrow [0,\infty{}) is a symmetrized and smoothed version of the Kullback–Leibler divergence D(P \parallel Q). It is defined by

{\rm JSD}(P \parallel Q)= \frac{1}{2}D(P \parallel M)+\frac{1}{2}D(Q \parallel M)

where M=\frac{1}{2}(P+Q)

If A is countable, a more general definition, allowing for the comparison of more than two distributions, is:

{\rm JSD}(P_1, P_2, \ldots, P_n) = H\left(\sum_{i=1}^n \pi_i P_i\right) - \sum_{i=1}^n \pi_i H(P_i)

where \pi_1, \pi_2, \ldots, \pi_n are the weights for the probability distributions P_1, P_2, \ldots, P_n and H(P) is the Shannon entropy for distribution P. For the two-distribution case described above,

P_1=P, P_2=Q, \pi_1 = \pi_2 = \frac{1}{2}.\


The Jensen–Shannon divergence is bounded by 1, given that one uses the base 2 logarithm.[5]

0 \leq {\rm JSD}( P \parallel Q ) \leq 1

For log base e, or ln, which is commonly used in statistical thermodynamics, the upper bound is ln(2):

0 \leq {\rm JSD}( P \parallel Q ) \leq \ln(2)

Relation to mutual information[edit]

Jensen–Shannon divergence is the mutual information between a random variable X from a mixture distribution M=\frac{P+Q}{2} and a binary indicator variable Z where Z = 1 if X is from P and Z = 0 if X is from Q.

I(X; Z) &= H(X) - H(X|Z)\\
&= -\sum M \log M + \frac{1}{2} \left[ \sum P \log P + \sum Q \log Q \right] \\
&= -\sum \frac{P}{2} \log M - \sum \frac{Q}{2} \log M + \frac{1}{2} \left[ \sum P \log P + \sum Q \log Q \right] \\
&= \frac{1}{2} \sum P \left( \log P - \log M\right ) + \frac{1}{2} \sum Q  \left( \log Q - \log M \right) \\
&= {\rm JSD}(P \parallel Q)

It follows from the above result that Jensen–Shannon divergence is bounded by 0 and 1 because mutual information is non-negative and bounded by H(Z) = 1.

One can apply the same principle to the joint and product of marginal distribution (in analogy to Kullback–Leibler divergence and mutual information) and to measure how reliably one can decide if a given response comes from the joint distribution or the product distribution—given that these are the only possibilities.[6]

Quantum Jensen–Shannon divergence[edit]

The generalization of probability distributions on density matrices allows to define quantum Jensen–Shannon divergence (QJSD).[7][8] It is defined for a set of density matrices (\rho_1,\ldots,\rho_n) and probability distribution \pi=(\pi_1,\ldots,\pi_n) as

{\rm QJSD}(\rho_1,\ldots,\rho_n)= S\left(\sum_{i=1}^n \pi_i \rho_i\right)-\sum_{i=1}^n \rho_i S(\pi_i)

where S(\pi_i) is the von Neumann entropy. This quantity was introduced in quantum information theory, where it is called the Holevo information: it gives the upper bound for amount of classical information encoded by the quantum states (\rho_1,\ldots,\rho_n) under the prior distribution \pi (see Holevo's theorem)[9] Quantum Jensen–Shannon divergence for \pi=\left(\frac{1}{2},\frac{1}{2}\right) and two density matrices is a symmetric function, everywhere defined, bounded and equal to zero only if two density matrices are the same. It is a square of a metric for pure states[10] but it is unknown whether the metric property holds in general.[8] The Bures metric is closely related to the quantum JS divergence; it is the quantum analog of the Fisher information metric.


The Jensen–Shannon divergence has been applied in bioinformatics and genome comparison,[11][12] in protein surface comparison,[13] and in the social sciences[14] and the quantitative study of history.[15]


  1. ^ Hinrich Schütze; Christopher D. Manning (1999). Foundations of Statistical Natural Language Processing. Cambridge, Mass: MIT Press. p. 304. ISBN 0-262-13360-1. 
  2. ^ Dagan, Ido; Lillian Lee; Fernando Pereira (1997). "Similarity-Based Methods For Word Sense Disambiguation". Proceedings of the Thirty-Fifth Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics: pp. 56–63. doi:10.3115/979617.979625. Retrieved 2008-03-09. 
  3. ^ Endres, D. M.; J. E. Schindelin (2003). "A new metric for probability distributions". IEEE Trans. Inf. Theory 49 (7): pp. 1858–1860. doi:10.1109/TIT.2003.813506. 
  4. ^ Ôsterreicher, F.; I. Vajda (2003). "A new class of metric divergences on probability spaces and its statistical applications". Ann. Inst. Statist. Math. 55 (3): pp. 639–653. doi:10.1007/BF02517812. 
  5. ^ Lin, J. (1991). "Divergence measures based on the shannon entropy". IEEE Transactions on Information Theory 37 (1): 145–151. doi:10.1109/18.61115. 
  6. ^ Schneidman, Elad; Bialek, W; Berry, M.J. 2nd (2003). "Synergy, Redundancy, and Independence in Population Codes". Journal of Neuroscience 23 (37): 11539–11553. PMID 14684857. 
  7. ^ Majtey, A.; Lamberti, P.; Prato, D. (2005). "Jensen-Shannon divergence as a measure of distinguishability between mixed quantum states". Physical Review A 72 (5). doi:10.1103/PhysRevA.72.052310. 
  8. ^ a b Briët, Jop; Harremoës, Peter (2009). "Properties of classical and quantum Jensen-Shannon divergence". Physical Review A 79 (5). doi:10.1103/PhysRevA.79.052311. 
  9. ^ Holevo, A. S. (1973), "Bounds for the quantity of information transmitted by a quantum communication channel", Problemy Peredachi Informatsii (in Russian) 9: 3–11 . English translation: Probl. Inf. Transm., 9, 177–183 (1975)) MR 456936
  10. ^ Braunstein, Samuel; Caves, Carlton (1994). "Statistical distance and the geometry of quantum states". Physical Review Letters 72 (22): 3439–3443. doi:10.1103/PhysRevLett.72.3439. PMID 10056200. 
  11. ^ Sims, GE; Jun, SR; Wu, GA; Kim, SH (2009). "Alignment-free genome comparison with feature frequency profiles (FFP) and optimal resolutions". Proceedings of the National Academy of Sciences of the United States of America 106 (8): 2677–82. doi:10.1073/pnas.0813249106. PMC 2634796. PMID 19188606. 
  12. ^ Itzkovitz, S; Hodis, E; Segal, E (2010). "Overlapping codes within protein-coding sequences". Genome Research 20 (11): 1582–9. doi:10.1101/gr.105072.110. PMC 2963821. PMID 20841429. 
  13. ^ Ofran, Y; Rost, B (2003). "Analysing six types of protein-protein interfaces". Journal of Molecular Biology 325 (2): 377–87. doi:10.1016/s0022-2836(02)01223-8. PMID 12488102. 
  14. ^ DeDeo, Simon; Hawkins, Robert X. D.; Klingenstein, Sara; Hitchcock, Tim (2013). "Bootstrap Methods for the Empirical Study of Decision-Making and Information Flows in Social Systems". "Entropy" 15 (6): 2246–2276. doi:10.3390/e15062246. 
  15. ^ Klingenstein, Sara; Hitchcock, Tim; DeDeo, Simon (2014). "The civilizing process in London’s Old Bailey". "Proceedings of the National Academy of Sciences" 111 (26): 9419–9424. doi:10.1073/pnas.1405984111. 

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