Wolfram Language & System 10.3 (2015)|Legacy Documentation
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 m〚i,j〛Probability[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.