Markov information source

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In mathematics, a Markov information source, or simply, a Markov source, is an information source whose underlying dynamics are given by a stationary finite Markov chain.

Formal definition[edit]

An information source is a sequence of random variables ranging over a finite alphabet Γ, having a stationary distribution.

A Markov information source is then a (stationary) Markov chain M, together with a function

f:S\to \Gamma

that maps states S in the Markov chain to letters in the alphabet Γ.

A unifilar Markov source is a Markov source for which the values f(s_k) are distinct whenever each of the states s_k are reachable, in one step, from a common prior state. Unifilar sources are notable in that many of their properties are far more easily analyzed, as compared to the general case.

Applications[edit]

Markov sources are commonly used in communication theory, as a model of a transmitter. Markov sources also occur in natural language processing, where they are used to represent hidden meaning in a text. Given the output of a Markov source, whose underlying Markov chain is unknown, the task of solving for the underlying chain is undertaken by the techniques of hidden Markov models, such as the Viterbi algorithm.

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