vb learning can converge to bad local minima. better initialization or better learning algorithm may help. for instance, deterministic annealing. also, sometimes changing the model might help. for instance, if one factors q(mu)q(Lambda), then re-formulating so that one gets q(mu,Lambda) might improve the posterior accuracy and the learning
aha, then I will change it to something like "implementing a student t distribution would require extra complexity which is out of the scope of this example"
doesn't the mixture node needs a class as a parameter not a network?
for the case of the constructing the t for the mixture
not sure what you mean, but the mixture in a mixture model is a different mixture than in student t construction. student t construction is based on an infinite mixture. it's basically just a particular gaussian-gamma joint distribution with the gamma distribution marginalized. but in vb approach, one doesn't marginalize the gamma analytically in order to keep the equations in
the exponential family form
i should write an example
what I thought was using two nodes to represent the t, but that won't do as an input to the mixture node... I just glanced at the links and the topic, it is new to me :)
I'm in a hurry for the release, so i'll postpone it
an example would be great :)
thanks a lot, Jaakko!
Hi @jluttine !I want to know is this result can only be shown by graph?Can we show probability density just like a format or some text?Thanks a lot!
Hello, I'm trying to learn a Gaussian mixture from Mnist but I get "ValueError: Must pass 2-d input", can't I use higher dimensional data?