Model Parameter Estimation

Estimates the HMM's parameters by maximizing the likelihood of a given observation sample. In case this sample is composed of incomplete data (observations only) the Baum-Welch algorithm[#!em_tut!#] is used. Multiple observation samples may also be used.

Usages: training( $<$it$>$ [,$<$thr$>$], $<$object_1$>$ [, ... ])
: training( $<$object_1$>$, $<$object_2$>$ )

where $<$it$>$ is the maximum number of iterations that the Baum-Welch algorithm will perform before stopping; $<$thr$>$ is the log-likelihood gain^11.3 threshold that, when reached, will stop the estimation algorithm; $<$object_1$>$ is the MTK object containing the observation sample; and $<$object_2$>$ is the MTK object containing the state path that generated the observations in $<$object_1$>$.

Output: Shows the progress of the estimation, by printing the current iteration and the likelihood of the observation sample used in the training, given the current parameter estimates. The values estimated for the parameters are automatically stored in the hmm object's attributes.