When observations are too many.

[Google Code Exporter]

It would be a kind of stupid question:

 There are many observations from each latent variables.
In this case, we can integrate all the observations(each k dimension) into one 
observation variable with k^n(n observations or sensors).

When k or n goes increase large enough, bijo goes small, which means alpha and 
beta goes to infinidecimal exponetially along with timeline, which make BW 
algorithm fails to learn parameter.

Here is my quesion, do you know how to learn parameter when obeservation prob. 
is low? or too many output in observation?

Original issue reported on code.google.com by [e-mail] on 11 Jul 2011 at 12:15