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 
object 
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 
A matrix specifying the probability distributions
of the observations for a hidden Markov model; ignored 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 
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 appears to be unlikely to occur 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()
, sim.hmm()
,
mps()
, pr()
,
viterbi()
1 2 3 4 5 6 7 8 9 10 11 12 13 14  # See the help for sim.hmm() for how to generate y.num and y.let.
## Not run:
fit.num < hmm(y.num,K=2,verb=TRUE)
v.1 < viterbi(object=fit.num)
v.2 < viterbi(y.num,tpm=P,Rho=R) # P and R as in the
# help for sim.hmm().
# The order of the states has gotten swapped; 3v.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(object=fit.let) # Works.
v.4 < viterbi(y.let,tpm=P,Rho=R) # Throws an error (R has no row names.)
## End(Not run)

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