Wolfram Language & System 10.0 (2014)|Legacy Documentation

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represents a discrete-time, finite-state Markov process with transition matrix m and initial state .

represents a Markov process with initial state probability vector .

represents a Markov process with transition matrix from the graph g.


  • DiscreteMarkovProcess is also known as a discrete-time Markov chain.
  • DiscreteMarkovProcess is a discrete-time and discrete-state random process.
  • The states of DiscreteMarkovProcess are integers between 1 and , where is the length of transition matrix m.
  • The transition matrix m specifies conditional transition probabilities mi,jProbability[x[k+1]jx[k]i], where is the state of the process at time k. »
  • A discrete Markov process can be seen as a random walk on a graph, where the probability of transitioning from state to state is specified by .
  • EstimatedProcess[data,DiscreteMarkovProcess[n]] indicates that a process with n states should be estimated.
  • The transition matrix in the case of a graph g is constructed to give equal probability of transitioning to each incident vertex.
  • DiscreteMarkovProcess allows m to be an × matrix with non-negative elements and rows that sum to 1, is an integer between 1 and , and is a vector of length of non-negative elements that sum to 1.
  • DiscreteMarkovProcess can be used with such functions as MarkovProcessProperties, PDF, Probability, and RandomFunction.
Introduced in 2012
| Updated in 2014