Maximum entropy and conditional probability

It is well-known that maximum entropy distributions, subject to appropriate moment constraints, arise in physics and mathematics. In an attempt to find a physical reason for the appearance of maximum entropy distributions, the following theorem is offered. The conditional distribution of X, given the empirical observation (1 /n)X:, ,/I ( X,) = (Y, where X, , X2, . are independent identically distributed random variables with common density g converges to fA( x) = e A’h(x)g( x) (suitably normalized), where X is chosen to satisfy jfx( x) h( x) dx = o. Thus the conditional distribution of a given random variable X is the (normalized) product of the maximum entropy distribution and the initial distribution. This distribution is the maximum entropy distribution when g is uniform. The proof of this and related results relies heavily on the work of Zabell and Lanford.