Fano's inequality

In information theory, Fano's inequality (also known as the Fano converse and the Fano lemma) relates the average information lost in a noisy channel to the probability of the categorization error. It was derived by Robert Fano in the early 1950s while teaching a Ph.D. seminar in information theory at MIT, and later recorded in his 1961 textbook.

It is used to find a lower bound on the error probability of any decoder as well as the lower bounds for minimax risks in density estimation.

Let the random variables and represent input and output messages with a joint probability . Let represent an occurrence of error; i.e., that , with being an approximate version of . Fano's inequality is

where denotes the support of ,

is the conditional entropy,

is the probability of the communication error, and

is the corresponding binary entropy.


Define an indicator random variable  , that indicates the event that our estimate   is in error,


Consider  . We can use the chain rule for entropies to expand this in two different ways


Equating the two


Expanding the right most term,  


Since   means  ; being given the value of   allows us to know the value of   with certainty. This makes the term  . On the other hand,   means that  , hence given the value of  , we can narrow down   to one of   different values, allowing us to upper bound the conditional entropy  . Hence


The other term,  , because conditioning reduces entropy. Because of the way   is defined,  , meaning that  . Putting it all together,


Because   is a Markov chain, we have   by the data processing inequality, and hence  , giving us


Alternative formulationEdit

Let   be a random variable with density equal to one of   possible densities  . Furthermore, the Kullback–Leibler divergence between any pair of densities cannot be too large,

  for all  

Let   be an estimate of the index. Then


where   is the probability induced by  


The following generalization is due to Ibragimov and Khasminskii (1979), Assouad and Birge (1983).

Let F be a class of densities with a subclass of r + 1 densities ƒθ such that for any θ ≠ θ


Then in the worst case the expected value of error of estimation is bound from below,


where ƒn is any density estimator based on a sample of size n.


  • P. Assouad, "Deux remarques sur l'estimation", Comptes Rendus de l'Académie des Sciences de Paris, Vol. 296, pp. 1021–1024, 1983.
  • L. Birge, "Estimating a density under order restrictions: nonasymptotic minimax risk", Technical report, UER de Sciences Économiques, Universite Paris X, Nanterre, France, 1983.
  • T. Cover, J. Thomas (1991). Elements of Information Theory. pp. 38–42. ISBN 978-0-471-06259-2.
  • L. Devroye, A Course in Density Estimation. Progress in probability and statistics, Vol 14. Boston, Birkhauser, 1987. ISBN 0-8176-3365-0, ISBN 3-7643-3365-0.
  • Fano, Robert (1968). Transmission of information: a statistical theory of communications. Cambridge, Mass: MIT Press. ISBN 978-0-262-56169-3. OCLC 804123877.
  • R. Fano, Fano inequality Scholarpedia, 2008.
  • I. A. Ibragimov, R. Z. Has′minskii, Statistical estimation, asymptotic theory. Applications of Mathematics, vol. 16, Springer-Verlag, New York, 1981. ISBN 0-387-90523-5