hsmm (version 0.3-4)

hsmm.viterbi: Hidden Semi-Markov Models

Description

Decoding states in Hidden Semi-Markov Models

Usage

hsmm.viterbi(x, 
                    od, 
                    rd, 
                    pi.par,
                    tpm.par,
                    od.par,
                    rd.par,
                    M = NA)

Arguments

x

the observed process, a vector of length $\tau$

od

character containing the name of the conditional distribution of the observations. For details see hsmm

rd

character containing the name of the runlength distribution (or sojourn time, dwell time distribution). For details see hsmm

pi.par

vector of length $J$ containing the values for the intitial probabilities of the semi-Markov chain

tpm.par

matrix of dimension $J x J$ containing the parameter values for the transition probability matrix of the embedded Markov chain. The diagonal entries must all be zero, absorbing states are not permitted

rd.par

list with the values for the parameters of the runlength distributions. For details see hsmm

od.par

list with the values for the parameters of the conditional observation distributions. For details see hsmm

M

positive integer containing the maximum runlength

Value

• call
  call
• path
  vector of length the $tau$ containing the most probable path of the underlying states

Details

The function hsmm.viterbi carries out the Viterbi algorithm. It derives the most probable state sequence by a dynamic programming technique. This procedure is often termed 'global decoding'.

See Also

hsmm, hsmm.sim, hsmm.smooth