Nonstationary hidden Markov model estimation
John
uebersaxjohn2000 at yahoo.com
Tue Mar 16 13:00:35 EST 2004
Does anyone know if the "Forward Algorithm" to estimate:
Pr(o1, o2, ..., oT | theta),
where:
o1, o2, ..., OT = observed states at T timepoints or sequence
positions
theta = a vector of parameters that define a hidden
Markov model
is correct if transition probability parameters are nonstationary?
That is, does the Forward Algorithm give the same value as the full
expansion of the likelihood function if the probabilities of moving
from state i to state j differ from one period/sequence position to
another?
I'm unable to find any reference to this despite a fairly thorough web
search, but it seems like an obvious question.
--
John Uebersax
More information about the Comp-bio
mailing list