Description Usage Arguments Details Value See Also Examples

Predict a state or the probability distribution over states in *n*
time steps.

1 2 3 |

`object` |
an |

`current_state` |
use a specified current state.
If |

`n` |
number of time steps. |

`probabilities` |
if |

`randomized` |
if |

`prior` |
add one to each transition count. This is equal to starting with a uniform prior for the transition count distribution, i.e. initially all transitions are equally likely. It also prevents the product of probabilities to be zero if a transition was never observed. |

Prediction is done using *A^n* where *A* is the transition
probability matrix maintained by the EMM.
Random tie-breaking is used.

The name of the predicted state or a vector with the probability distribution over all states.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
data("EMMTraffic")
emm <- EMM(measure="eJaccard", threshold=0.2)
emm <- build(emm, EMMTraffic)
#plot(emm) ## plot graph
## Predict state starting an state 1 after 1, 2 and 100 time intervals
## Note, state 7 is an absorbing state.
predict(emm, n=1, current_state="1")
predict(emm, n=2, current_state="1")
predict(emm, n=100, current_state="1")
## Get probability distribution
predict(emm, n=2, current_state="1", probabilities = TRUE)
``` |

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