over [],[, p 344-346,].
A special feature of the algorithm is the guaranteed convergence
.

To describe the
*Baum-Welch algorithm*, ( also known as *Forward-Backward
algorithm*), we need to define two
more auxiliary variables, in addition to the forward and backward
variables defined in a previous section. These variables can however
be expressed in terms of the forward and backward variables.

First one of those variables is defined as the probability of
being in state *i* at *t*=*t* and in state *j* at *t*=*t*+1. Formally,

This is the same as,

Using forward and backward variables this can be expressed as,

The second variable is the a posteriori probability,

that is the probability of being in state *i* at *t*=*t*, given the
observation sequence and the model. In forward and backward variables
this can be expressed by,

One can see that the relationship between and is given by,

Now it is possible to describe the Baum-Welch learning process, where
parameters of the HMM is updated in such a way to maximize the quantity,
. Assuming a starting model
,we calculate the ' 's and ' 's using the
recursions 1.5 and 1.2, and then ' 's and
' 's using 1.12 and 1.15. Next step is to update
the HMM parameters according to eqns 1.16 to 1.18,
known as *re-estimation formulas*.

These reestimation formulas can easily be modified to deal with the continuous density case too.

Fri May 10 20:35:10 MET DST 1996