Description Usage Arguments Value Author(s) References See Also Examples

Viterbi training is a faster but less reliable alternative to Baum-Welch for parameter estimation.

1 2 3 |

`hmm` |
Object of class |

`obs` |
List of observation sequences. |

`max.iter` |
Maximum number of iterations. |

`eps` |
Minimum change in log likelihood between successive iterations. |

`df` |
If this is |

`trans.prior` |
Prior distribution of transition probabilities. A prior can be specified
either by providing a matrix with transition probabilities or by setting |

`init.prior` |
Prior distribution of initial state probabilities. A prior can be specified
either by providing a vector with initial state probabilities or by setting |

`keep.models` |
A character string interpreted as a file name. If |

`verbose` |
Level of verbosity. Allows some control over the amount of output printed to the console. |

Object of class `hmm`

with the pest parameter estimates (in terms of likelihood) found
during the fitting procedure.

Peter Humburg

Juang, B.-H. and Rabiner, L. R. 1990
A segmental k-means algorithm for estimating parameters of hidden Markov models.
*IEEE Transactions on Acoustics, Speech, and Signal Processing*, **38**(9), 1639–1641.

`viterbi`

, `baumWelch`

, `viterbiEM`

, `hmm.setup`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
## create two state HMM with t distributions
state.names <- c("one","two")
transition <- c(0.035, 0.01)
location <- c(1, 2)
scale <- c(1, 1)
df <- c(4, 6)
hmm1 <- getHMM(list(a=transition, mu=location, sigma=scale, nu=df),
state.names)
## generate observation sequences from model
obs.lst <- list()
for(i in 1:50) obs.lst[[i]] <- sampleSeq(hmm1, 100)
## fit an HMM to the data (with fixed degrees of freedom)
hmm2 <- hmm.setup(obs.lst, state=c("one","two"), df=5)
hmm2.fit <- viterbiTraining(hmm2, obs.lst, max.iter=20, df=5, verbose=1)
## fit an HMM to the data, this time estimating the degrees of freedom
hmm3.fit <- viterbiTraining(hmm2, obs.lst, max.iter=20, verbose=1)
``` |

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