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

Calculates “the” most probable state sequence underlying each of one or more replicate observation sequences.

1 |

`y` |
The observations for which the underlying most probable
hidden states are required. May be a sequence of observations,
or a list each entry of which constitutes an independent sequence
of observations. If |

`model` |
An object describing a hidden Markov model, as
fitted to the data set |

`tpm` |
The transition probability matrix for a hidden
Markov model; ignored if |

`Rho` |
An object specifying the probability distributions
of the observations for a hidden Markov model. See
If |

`ispd` |
The initial state probability distribution for a hidden
Markov model; ignored if |

`log` |
Logical scalar. Should logarithms be used in the
recursive calculations of the probabilities involved in the
Viterbi algorithm, so as to avoid underflow? If |

`warn` |
Logical scalar; should a warning be issued if |

Applies the Viterbi algorithm to calculate “the” most probable robable state sequence underlying each observation sequences.

If `y`

consists of a single observation sequence, the
value is the underlying most probable observation sequence,
or a matrix whose columns consist of such sequences if there
is more than one (equally) most probable sequence.

If `y`

consists of a list of observation sequences, the
value is a list each entry of which is of the form described
above.

There *may* be more than one equally most probable state
sequence underlying a given observation sequence. This phenomenon
can occur but appears to be unlikely to do so in practice.

The correction made to the code so as to avoid underflow problems was made due to an inquiry and suggestion from Owen Marshall.

Rolf Turner
[email protected]

Rabiner, L. R., "A tutorial on hidden Markov models and selected applications in speech recognition," Proc. IEEE vol. 77, pp. 257 – 286, 1989.

`hmm()`

, `rhmm()`

,
`mps()`

, `pr()`

,
`viterbi()`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
# See the help for rhmm() for how to generate y.num and y.let.
## Not run:
fit.num <- hmm(y.num,K=2,verb=TRUE)
v.1 <- viterbi(model=fit.num)
v.2 <- viterbi(y.num,tpm=P,Rho=R) # P and R as in the
# help for rhmm().
# The order of the states has gotten swapped; 3-v.1[[1]] is much
# more similar to v.2[[1]] than is v.1[[1]].
fit.let <- hmm(y.let,K=2,verb=TRUE)
v.3 <- viterbi(model=fit.let) # Works.
v.4 <- viterbi(y.let,tpm=P,Rho=R) # Throws an error (R has no row names.)
## End(Not run)
``` |

hmm.discnp documentation built on Nov. 12, 2018, 1:04 a.m.

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