 Markov information source

A Markov information source uses a stationary finite Markov chain to emit symbols. The emitted symbol can be a function of the state. If it does not identify that state, the observed sequence need not itself be a Markov chain. The examples here use a fully observed Markov source, which makes the next-symbol uncertainty directly computable from the transition matrix.

A Markov information source is a stochastic model used to describe systems or processes that exhibit Markovian properties, particularly the memoryless property. In simpler terms, a Markov information source is a type of probabilistic model in which the future state of the process depends only on the current state and not on the sequence of events that preceded it.