Hi,
I would like to know as to what is the convergence criteria that is being used in GeNIe to learn the parameters using an EM algorithm.
My qns.
1. Is it just fixed to a large no. so the algorithm runs for so many no. of iterations or some other criteria? How do i change this criteria if needed??
2. What is the significance of the Log p value that is returned after learning the network parameters??
Thanks.
Convergence criteria for EM algorithm?
Re: Convergence criteria for EM algorithm?
1. It looks at the relative improvement of the log likelihood. When it's below a certain threshold, learning is stopped. This criteria cannot be changed at the moment.
2. It is the data log likelihood of the learned parameters.
2. It is the data log likelihood of the learned parameters.