Fitting data with multiple attributes

Questions
#1

I recently started to use PyMC3, and have a question.

Is it possible to use PyMC3 to fit data in which each sample (this could be a discrete time sample, for example) has measurements of several different quantities associated with it, all of which are predicted by a different function of the parameters of an underlying model). That is, each sample is described by a vector, rather than a scalar.

If this is possible, how would I need to modify a statement such as

Y_obs = Normal(‘Y_obs’, mu=mu, sd=sigma, observed=Y)

for example?

#2

You can either define it as a MvNormal, or a Normal with mu and/or sigma being the same size vector as Y. If would be equivalent as having Y_obs1, Y_obs2… etc.

#3

Do you have an example you would be willing to share?

#4

You can have a look at https://osf.io/rsfdv/. You can find the background information about it in the code and the accompany preprint.