||This article provides insufficient context for those unfamiliar with the subject. (February 2012)|
Let A be a set of probability distributions over an alphabet X, and let q be an arbitrary distribution over X (where q may or may not be in A). Suppose we draw n i.i.d. samples from q, represented by the vector . Further, let us ask that the empirical distribution, , of the samples falls within the set A -- formally, we write . Then,
- is shorthand for , and
- is the information projection of q onto A.
In words, the probability of drawing an atypical distribution is proportional to the KL distance from the true distribution to the atypical one; in the case that we consider a set of possible atypical distributions, there is a dominant atypical distribution, given by the information projection.
Furthermore, if A is a closed set,
||This article includes a list of references, related reading or external links, but its sources remain unclear because it lacks inline citations. (February 2012)|
- Cover, Thomas M.; Thomas, Joy A. (2006). Elements of Information Theory (2 ed.). Hoboken, New Jersey: Wiley Interscience. p. 362.
- Sanov, I. N. (1957) "On the probability of large deviations of random variables". Mat. Sbornik 42, 11–44.
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