Hello,

I was wondering if you can tell me how I need to put initial and consequent values for the approximate inference of Variational Mixture of Gaussians based on Bishop's Pattern Recognition and machine Learning (pages 478, 479) where he says: "Then
in the subsequent variational equivalent of the M step, we keep these responsibilities fixed and use them to re-compute the variational
distribution over the parameters using (10.57) and (10.59).".

I am not sure how exactly implement E and M steps regarding theses information.

Thanks