The
behavior of an
information ally closed and generative system that is specified by transition probabilities (
see probability ) between that system's
states. It is named after A. A. Markov who at the turn of the century studied poetry and other texts as stochastic sequences of
characters (
symbol s, letters, syllables, and words). The probabilities of a Markov chain are usually entered into a transition
matrix indicating which state or symbol follows which other state or symbol. The order (
see ordinality ) of a Markov chain corresponds to the number of states or symbols from which probabilities are defined to a successor. Ordinarily, Markov chains are
state determined or of the first order. Higher orders are
history determined. An unequal
distribution of transition probabilities is a mark of a Markov chain's
redundancy and a prerequisite of predictability (
see information theory ). (
Krippendorff )
