1\name{viterbi} 2\alias{viterbi} 3\title{Viterbi algorithm} 4\description{This function calculates the optimal hidden states sequence using Viterbi's algorithm.} 5\usage{ 6viterbi(HMM, obs) 7} 8\arguments{ 9 \item{HMM}{a HMMClass or a HMMFitClass object.} 10 \item{obs}{The vector, matrix, data frame, list of vectors or list of matrices of observations.} 11} 12\value{a viterbiClass object which is a list with: 13\item{States}{Sequence of hidden states in 1...nStates} 14\item{logViterbiScore}{logarithm of the Viterbi's Score.} 15\item{logProbSeq}{logarithm of probability of having the sequence of states conditionally to having the observations.} 16} 17\examples{ 18 data(n1d_3s) 19 ResFit <- HMMFit(obs_n1d_3s, nStates=3) 20 VitPath <- viterbi(ResFit, obs_n1d_3s) 21} 22\references{ 23 Among hundreds of tutorials, you can have a look to use \cr 24 Phil Blunsom (2004) \emph{ Hidden Markov Models. } 25 \url{http://www.cs.mu.oz.au/460/2004/materials/hmm-tutorial.pdf}} 26 27\seealso{\code{\link{HMMSet}}, \code{\link{HMMFit}}} 28\keyword{hplot} 29