Possible Performance Issue Using Sparse Matrices in Bipartite Noisy-Or Model

Questions
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3

Junpeng’s modification improves things:

with pm.Model() as model:
    theta_h = pm.Uniform("theta_h",lower=0,upper=1,shape=n)
    theta_leak = pm.Beta("theta_leak",alpha=2,beta=20,shape=m,testval=0.05)
    theta_i = pm.Beta("theta_i",alpha=2,beta=20,shape=n, testval = 0.05)

    # Log transformed
    wi = pm.math.log(theta_i)
    w0 = pm.math.log(theta_leak)
    AHwi = tt.dot(tt.dot(A_dense, tt.diag(theta_h)), wi)
    p_v_given_h = 1 - pm.math.exp(w0 + AHwi)
    v = pm.Bernoulli("v",p=p_v_given_h,shape=m,observed=observed)
    trace = pm.sample()

With 1000 visible nodes and 10 hidden nodes, I’m getting about 22 it/s.
At 10000 visible and 100 hidden, sampling seems to be about 1.5 it/s.

Given that this really isn’t all that much data, I have to imagine there are ways to formulating this problem that will result in faster sampling speeds.