I am trying to implement a model with a Multivariate Normal likelihood and I am estimating the covariance matrix using the LKJCholeskyCov prior. However, I would like to define a different distribution for each one of the standard deviations. I read in the documentation that the
sd_dist parameter must be a positive scalar or vector distribution for the standard deviations. So, I am defining a different distribution for each one and I am combining them using
Here is the code:
with pm.Model() as model: sigmas_dists= [ pm.InverseGamma.dist(alpha= 40, beta=80), pm.InverseGamma.dist(alpha= 100, beta=500) ] stds = at.stack( sigmas_dists ) packed_L = pm.LKJCholeskyCov('packed_L', n=2, eta=2., sd_dist= stds , compute_corr=False) L = pm.expand_packed_triangular(2, packed_L) cov = L.dot(L.T) ...
But I am getting the following error
TypeError: sd_dist must be a scalar or vector distribution variable
What am I doing wrong?
Thank you in advance!