hsmm.viterbi {hsmm} | R Documentation |
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 |
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'.
Value
call |
call |
path |
vector of length the tau containing the most probable path of the underlying states |
See Also
hsmm
, hsmm.sim
, hsmm.smooth
[Package
hsmm version 0.3-4
Index]