The decomposition of a central matrix involved in this construct is governed by the Chapman-Kolmogorov equations.
The decomposition of a central matrix involved in this construct is governed by the Chapman-Kolmogorov equations. This construct admits a unique decomposition into the union of the set of transient states and irreducible closed sets of persistent states. If for one of these constructs there exists a distribution which is in detailed balance, then this construct is reversible in equilibrium. A most-likely sequence of states for this type of process can be found by the Viterbi algorithm, while exact inference can be done by the forward algorithm for processes of this type with "hidden" states. State transitions in this type of process are determined solely by the beginning state, the end state, and the transition matrix. Exemplified by the random walk, for 10 points, identify this type of stochastic process, whose state transitions are independent of its history.
The decomposition of a central matrix involved in this construct is governed by the Chapman-Kolmogorov equations.
Markov process or chain [prompt on "stochastic" before mention; accept "Wiener process" before "transient"] 