Estep {HiddenMarkov} | R Documentation |
E-Step of EM Algorithm for DTHMM
Description
Performs the expectation step of the EM algorithm for a dthmm
process. This function is called by the BaumWelch
function. The Baum-Welch algorithm referred to in the HMM literature is a version of the EM algorithm.
Usage
Estep(x, Pi, delta, distn, pm, pn = NULL)
Arguments
x |
is a vector of length |
Pi |
is the current estimate of the |
distn |
is a character string with the distribution name, e.g. |
pm |
is a list object containing the current (Markov dependent) parameter estimates associated with the distribution of the observed process (see |
pn |
is a list object containing the observation dependent parameter values associated with the distribution of the observed process (see |
delta |
is the current estimate of the marginal probability distribution of the |
Details
Let be one if
and zero otherwise. Further, let
be one if
and
, and zero otherwise. Let
contain the complete observed process. Then, given the current model parameter estimates, the returned value
u[i,j]
is
and v[i,j,k]
is
where and
.
Value
A list
object is returned with the following components.
u |
an |
v |
an |
LL |
the current value of the log-likelihood. |
Author(s)
The algorithm has been taken from Zucchini (2005).
References
Cited references are listed on the HiddenMarkov manual page.