I want to model an underlying function f:
f ~ MvN(mu, Sigma), where Sigma is fixed.
and I assume that my observations y are i.i.d with Gaussian Noise:
y_i ~ N(f_i, eps)
with pm.Model() as model: epsilon = pm.HalfNormal('epsilon', sd=1) fs = pm.MvNormal('fs', mu=tt.zeros(len(ys)), cov=Sigma) ys = pm.MvNormal('ys', mu=fs, cov=epsilon*tt.eye(len(fs)), observed=ys) # or: ys = pm.Normal('ys', mu=fs, sd=epsilon, shape=len(_ys), observed=ys) print(pm.find_MAP())
which yields the error “Invalid dimension for value: 0”. ys has shape (n,)
I tried different things and cannot get it to work. I’d be very thankful for some help