 Maximum entropy probability distribution

In statistics and information theory, a maximum entropy probability distribution is a probability distribution whose entropy is at least as great as that of all other members of a specified class of distributions.
According to the principle of maximum entropy, if nothing is known about a distribution except that it belongs to a certain class, then the distribution with the largest entropy should be chosen as the default. The motivation is twofold: first, maximizing entropy minimizes the amount of prior information built into the distribution; second, many physical systems tend to move towards maximal entropy configurations over time.
Contents
Definition of entropy
Further information: Entropy (information theory)If X is a discrete random variable with distribution given by
then the entropy of X is defined as
If X is a continuous random variable with probability density p(x), then the entropy of X is sometimes defined as^{[1]}^{[2]}^{[3]}
where p(x) log p(x) is understood to be zero whenever p(x) = 0. In connection with maximum entropy distributions, this form of definition is often the only one given, or at least it is taken as the standard form. However, it is recognisable as the special case m=1 of the more general definition
which is discussed in the articles Entropy (information theory) and Principle of maximum entropy.
The base of the logarithm is not important as long as the same one is used consistently: change of base merely results in a rescaling of the entropy. Information theoreticians may prefer to use base 2 in order to express the entropy in bits; mathematicians and physicists will often prefer the natural logarithm, resulting in a unit of nats or nepers for the entropy.
Examples of maximum entropy distributions
A table of examples of maximum entropy distributions is given in Park & Bera (2009)^{[4]}
Given mean and standard deviation: the normal distribution
The normal distribution N(μ,σ^{2}) has maximum entropy among all realvalued distributions with specified mean μ and standard deviation σ. Therefore, the assumption of normality imposes the minimal prior structural constraint beyond these moments.(See the differential entropy article for a derivation.)
Uniform and piecewise uniform distributions
The uniform distribution on the interval [a,b] is the maximum entropy distribution among all continuous distributions which are supported in the interval [a, b] (which means that the probability density is 0 outside of the interval).
More generally, if we're given a subdivision a=a_{0} < a_{1} < ... < a_{k} = b of the interval [a,b] and probabilities p_{1},...,p_{k} which add up to one, then we can consider the class of all continuous distributions such that
The density of the maximum entropy distribution for this class is constant on each of the intervals [a_{j1},a_{j}); it looks somewhat like a histogram.
The uniform distribution on the finite set {x_{1},...,x_{n}} (which assigns a probability of 1/n to each of these values) is the maximum entropy distribution among all discrete distributions supported on this set.
Positive and given mean: the exponential distribution
The exponential distribution with mean 1/λ is the maximum entropy distribution among all continuous distributions supported in [0,∞) that have a mean of 1/λ.
In physics, this occurs when gravity acts on a gas that is kept at constant pressure and temperature: if X describes the height of a molecule, then the variable X is exponentially distributed (which also means that the density of the gas depends on height proportional to the exponential distribution). The reason: X is clearly positive and its mean, which corresponds to the average potential energy, is fixed. Over time, the system will attain its maximum entropy configuration, according to the second law of thermodynamics.
Discrete distributions with given mean
Among all the discrete distributions supported on the set {x_{1},...,x_{n}} with mean μ, the maximum entropy distribution has the following shape:
where the positive constants C and r can be determined by the requirements that the sum of all the probabilities must be 1 and the expected value must be μ.
For example, if a large number N of dice is thrown, and you are told that the sum of all the shown numbers is S. Based on this information alone, what would be a reasonable assumption for the number of dice showing 1, 2, ..., 6? This is an instance of the situation considered above, with {x_{1},...,x_{6}} = {1,...,6} and μ = S/N.
Finally, among all the discrete distributions supported on the infinite set {x_{1},x_{2},...} with mean μ, the maximum entropy distribution has the shape:
where again the constants C and r were determined by the requirements that the sum of all the probabilities must be 1 and the expected value must be μ. For example, in the case that x_{k} = k, this gives
Circular random variables
For a continuous random variable θ_{i} distributed about the unit circle, the Von Mises distribution maximizes the entropy when given the real and imaginary parts of the first circular moment^{[5]} or, equivalently, the circular mean and circular variance.
When given the mean and variance of the angles θ_{i} modulo 2π, the wrapped normal distribution maximizes the entropy^{[5]}.
A theorem by Boltzmann
All the above examples are consequences of the following theorem by Ludwig Boltzmann.
Continuous version
Suppose S is a closed subset of the real numbers R and we're given n measurable functions f_{1},...,f_{n} and n numbers a_{1},...,a_{n}. We consider the class C of all continuous random variables which are supported on S (i.e. whose density function is zero outside of S) and which satisfy the n expected value conditions
If there is a member in C whose density function is positive everywhere in S, and if there exists a maximal entropy distribution for C, then its probability density p(x) has the following shape:
where the constants c and λ_{j} have to be determined so that the integral of p(x) over S is 1 and the above conditions for the expected values are satisfied.
Conversely, if constants c and λ_{j} like this can be found, then p(x) is indeed the density of the (unique) maximum entropy distribution for our class C.
This theorem is proved with the calculus of variations and Lagrange multipliers.
Discrete version
Suppose S = {x_{1},x_{2},...} is a (finite or infinite) discrete subset of the reals and we're given n functions f_{1},...,f_{n} and n numbers a_{1},...,a_{n}. We consider the class C of all discrete random variables X which are supported on S and which satisfy the n conditions
If there exists a member of C which assigns positive probability to all members of S and if there exists a maximum entropy distribution for C, then this distribution has the following shape:
where the constants c and λ_{j} have to be determined so that the sum of the probabilities is 1 and the above conditions for the expected values are satisfied.
Conversely, if constants c and λ_{j} like this can be found, then the above distribution is indeed the maximum entropy distribution for our class C.
This version of the theorem can be proved with the tools of ordinary calculus and Lagrange multipliers.
Caveats
Note that not all classes of distributions contain a maximum entropy distribution. It is possible that a class contain distributions of arbitrarily large entropy (e.g. the class of all continuous distributions on R with mean 0 but arbitrary standard deviation), or that the entropies are bounded above but there is no distribution which attains the maximal entropy (e.g. the class of all continuous distributions X on R with E(X) = 0 and E(X^{2}) = E(X^{3}) = 1^{[citation needed]}).
It is also possible that the expected value restrictions for the class C force the probability distribution to be zero in certain subsets of S. In that case our theorem doesn't apply, but one can work around this by shrinking the set S.
See also
Notes
 ^ Williams, D. (2001) Weighing the Odds Cambridge UP ISBN 052100618x (pages 197199)
 ^ Bernardo, J.M., Smith, A.F.M. (2000) Bayesian Theory'.' Wiley. ISBN 047149464x (pages 209, 366)
 ^ O'Hagan, A. (1994) Kendall's Advanced Theory of statistics, Vol 2B, Bayesian Inference, Edward Arnold. ISBN 0340529229 (Section 5.40)
 ^ Park, Sung Y.; Bera, Anil K. (2009). "Maximum entropy autoregressive conditional heteroskedasticity model". Journal of Econometrics (Elsevier): 219–230. http://www.wise.xmu.edu.cn/Master/Download/..%5C..%5CUploadFiles%5Cpapermasterdownload%5C2009519932327055475115776.pdf. Retrieved 20110602.
 ^ ^{a} ^{b} Jammalamadaka, S. Rao; SenGupta, A. (2001). Topics in circular statistics. New Jersey: World Scientific. ISBN 9810237782. http://books.google.com/books?id=sKqWMGqQXQkC&printsec=frontcover&dq=Jammalamadaka+Topics+in+circular&hl=en&ei=iJ3QTe77NKL00gGdyqHoDQ&sa=X&oi=book_result&ct=result&resnum=1&ved=0CDcQ6AEwAA#v=onepage&q&f=false. Retrieved 20110515.
References
 T. M. Cover and J. A. Thomas, Elements of Information Theory, 1991. Chapter 11.
 I. J. Taneja, Generalized Information Measures and Their Applications 2001. Chapter 1
Categories: Entropy and information
 Continuous distributions
 Discrete distributions
 Particle statistics
 Types of probability distributions
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