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In probability theory, an ƒ-divergence is a function Df (P  || Q) that measures the difference between two probability distributions P and Q. It helps the intuition to think of the divergence as an average, weighted by the function f, of the odds ratio given by P and Q[citation needed].

These divergences were introduced by Alfréd Rényi[1] in the same paper where he introduced the well-known Rényi entropy. He proved that these divergences decrease in Markov Processes. f-divergences were studied further independently by Csiszár (1963), Morimoto (1963) and Ali & Silvey (1966) and are sometimes known as Csiszár ƒ-divergences, Csiszár-Morimoto divergences or Ali-Silvey distances.


Let P and Q be two probability distributions over a space Ω such that P is absolutely continuous with respect to Q. Then, for a convex function f such that f(1) = 0, the f-divergence of P from Q is defined as

If P and Q are both absolutely continuous with respect to a reference distribution μ on Ω then their probability densities p and q satisfy dP = p dμ and dQ = q dμ. In this case the f-divergence can be written as

The f-divergences can be expressed using Taylor series and rewritten using a weighted sum of chi-type distances (Nielsen & Nock (2013)).

Instances of f-divergences[edit]

Many common divergences, such as KL-divergence, Hellinger distance, and total variation distance, are special cases of f-divergence, coinciding with a particular choice of f. The following table lists many of the common divergences between probability distributions and the f function to which they correspond (cf. Liese & Vajda (2006)).

Divergence Corresponding f(t)
reverse KL-divergence
squared Hellinger distance
Total variation distance
Pearson -divergence
Neyman -divergence (reverse Pearson)
Jensen-Shannon Divergence
α-divergence (other designation)

The function is defined up to the summand , where is any constant.


  • Non-negativity: the ƒ-divergence is always positive; it's zero if and only if the measures P and Q coincide. This follows immediately from Jensen’s inequality:
  • Monotonicity: if κ is an arbitrary transition probability that transforms measures P and Q into Pκ and Qκ correspondingly, then
    The equality here holds if and only if the transition is induced from a sufficient statistic with respect to {P, Q}.
  • Joint Convexity: for any 0 ≤ λ ≤ 1
    This follows from the convexity of the mapping on .

In particular, the monotonicity implies that if a Markov process has a positive equilibrium probability distribution then is a monotonic (non-increasing) function of time, where the probability distribution is a solution of the Kolmogorov forward equations (or Master equation), used to describe the time evolution of the probability distribution in the Markov process. This means that all f-divergences are the Lyapunov functions of the Kolmogorov forward equations. Reverse statement is also true: If is a Lyapunov function for all Markov chains with positive equilibrium and is of the trace-form () then , for some convex function f.[2][3] For example, Bregman divergences in general do not have such property and can increase in Markov processes.[4]

See also[edit]


  • Csiszár, I. (1963). "Eine informationstheoretische Ungleichung und ihre Anwendung auf den Beweis der Ergodizitat von Markoffschen Ketten". Magyar. Tud. Akad. Mat. Kutato Int. Kozl. 8: 85–108.
  • Morimoto, T. (1963). "Markov processes and the H-theorem". J. Phys. Soc. Jpn. 18 (3): 328–331. Bibcode:1963JPSJ...18..328M. doi:10.1143/JPSJ.18.328.
  • Ali, S. M.; Silvey, S. D. (1966). "A general class of coefficients of divergence of one distribution from another". Journal of the Royal Statistical Society, Series B. 28 (1): 131–142. JSTOR 2984279. MR 0196777.
  • Csiszár, I. (1967). "Information-type measures of difference of probability distributions and indirect observation". Studia Scientiarum Mathematicarum Hungarica. 2: 229–318.
  • Csiszár, I.; Shields, P. (2004). "Information Theory and Statistics: A Tutorial" (PDF). Foundations and Trends in Communications and Information Theory. 1 (4): 417–528. doi:10.1561/0100000004. Retrieved 2009-04-08.
  • Liese, F.; Vajda, I. (2006). "On divergences and informations in statistics and information theory". IEEE Transactions on Information Theory. 52 (10): 4394–4412. doi:10.1109/TIT.2006.881731.
  • Nielsen, F.; Nock, R. (2013). "On the Chi square and higher-order Chi distances for approximating f-divergences". IEEE Signal Processing Letters. 21: 10–13. arXiv:1309.3029. Bibcode:2014ISPL...21...10N. doi:10.1109/LSP.2013.2288355.
  • Coeurjolly, J-F.; Drouilhet, R. (2006). "Normalized information-based divergences". arXiv:math/0604246.
  1. ^ Rényi, Alfréd (1961). On measures of entropy and information (PDF). The 4th Berkeley Symposium on Mathematics, Statistics and Probability, 1960. Berkeley, CA: University of California Press. pp. 547–561. Eq. (4.20)
  2. ^ Gorban, Pavel A. (15 October 2003). "Monotonically equivalent entropies and solution of additivity equation". Physica A. 328 (3–4): 380–390. arXiv:cond-mat/0304131. doi:10.1016/S0378-4371(03)00578-8.
  3. ^ Amari, Shun'ichi (2009). Leung, C.S.; Lee, M.; Chan, J.H. (eds.). Divergence, Optimization, Geometry. The 16th International Conference on Neural Information Processing (ICONIP 20009), Bangkok, Thailand, 1--5 December 2009. Lecture Notes in Computer Science, vol 5863. Berlin, Heidelberg: Springer. pp. 185--193. doi:10.1007/978-3-642-10677-4_21.
  4. ^ Gorban, Alexander N. (29 April 2014). "General H-theorem and Entropies that Violate the Second Law". Entropy. 16 (5): 2408–2432. arXiv:1212.6767. doi:10.3390/e16052408.