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Create discrete-time Markov chain

`dtmc`

creates a discrete-time, finite-state,
time-homogeneous Markov chain from a specified state transition matrix.

After creating a `dtmc`

object, you can analyze the structure and
evolution of the Markov chain, or visualize the Markov chain various ways, using the
object
functions.

`mc = dtmc(P)`

`mc = dtmc(P,'StateNames',stateNames)`

creates a discrete-time Markov chain object `mc`

= dtmc(`P`

)`mc`

specified by
the state transition matrix `P`

.

optionally associates names `mc`

= dtmc(`P`

,`'StateNames'`

,stateNames)`stateNames`

to the states.

Determine Markov Chain Structure

`asymptotics` | Determine Markov chain asymptotics |

`isergodic` | Check Markov chain for ergodicity |

`isreducible` | Check Markov chain for reducibility |

`classify` | Classify Markov chain states |

`lazy` | Adjust Markov chain state inertia |

`subchain` | Extract Markov subchain |

Describe Markov Chain Evolution

`redistribute` | Compute Markov chain redistributions |

`simulate` | Simulate Markov chain state walks |

You also can create a Markov chain object using `mcmix`

.

[1]
Gallager, R.G. *Stochastic Processes: Theory for Applications.* Cambridge, UK: Cambridge University Press, 2013.

[2]
Haggstrom, O. *Finite Markov Chains and Algorithmic Applications.* Cambridge, UK: Cambridge University Press, 2002.

[3]
Hamilton, J. D. *Time Series Analysis*. Princeton, NJ: Princeton University Press, 1994.

[4]
Norris, J. R. *Markov Chains.* Cambridge, UK: Cambridge University Press, 1997.

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