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

The Baum-Welch algorithm [Baum et al., 1970] is a well established method for estimating parameters of HMMs. It represents the EM algorithm [Dempster et al., 1977] for the specific case of HMMs. The formulation of the Baum-Welch algorithm used in this implementation is based on the description given by Rabiner [1989].

1 2 3 |

`hmm` |
An object of class |

`obs` |
A list of observation sequences. |

`max.iter` |
Maximum number of iterations. (optional) |

`eps` |
Minimum difference in log likelihood between iterations. Default: 0.01 |

`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 |

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

Returns the HMM with optimised parameters.

Peter Humburg

Baum, L. E. and Petrie, T. and Soules, G. and Weiss, N. 1970
A maximization technique occuring in the statistical analysis of
probabilistic functions of markov chains.
*The Annals of Mathematical Statistics*, **41**(1), 164–171.

Dempster, A. P. and Laird, N. M. and Rubin, D. B. 1977
Maximum likelihood for incomplete data via the EM algorithm.
*Journal of the Royal Statistical Society*, Series B, **39**(1).

Rabiner, L. R. 1989
A tutorial on hidden Markov models and selected applications in speech recognition.
*Proceedings of the IEEE*, **77**(2), 257–286.

`viterbiTraining`

, `viterbiEM`

, `getHMM`

, `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 <- baumWelch(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 <- baumWelch(hmm2, obs.lst, max.iter=20, verbose=1)
``` |

tileHMM documentation built on May 30, 2017, 3:41 a.m.

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