Scipy.stats distributions cdfs

Questions
2

Use as_op is likely very inefficient, there is an effort to introduce cdf to the distribution class but progress is a bit slow: https://github.com/pymc-devs/pymc3/pull/2688

Having said that, it is possible to make it work with as_op as well, you can try:

@as_op(itypes=[tt.dmatrix, tt.dvector, tt.dvector], otypes=[tt.dmatrix])
def fn(P, alphas, betas):
    return beta.ppf(norm.cdf(P), alphas, betas)

import theano

with pm.Model() as model:
    packed_L = pm.LKJCholeskyCov('packed_L',
                                 n=S, eta=1,
                                 sd_dist=pm.HalfCauchy.dist(2.5))
    chol = pm.expand_packed_triangular(S, packed_L)
    M = pm.MvNormal('M', mu=hyper_mu, chol=chol, shape=(S, S))
    Q = fn(M, theano.shared(alphas.astype('float64')), theano.shared(betas.astype('float64')))
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