student t distribution is possible with bayespy. one just needs to construct it as a combination of gamma+gaussian. so one could implement mixture of student t with bayespy
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
Rodrigo Gadea
@rodrigogadea_twitter
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"
Jaakko Luttinen
@jluttine
yep
Rodrigo Gadea
@rodrigogadea_twitter
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
Jaakko Luttinen
@jluttine
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
some day
Rodrigo Gadea
@rodrigogadea_twitter
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!
Jaakko Luttinen
@jluttine
np
DingBoom
@DingBoom
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!