I have variables called Target and Shape which are 1000 X 3. T is my rotation matrix that has random variables (partially shown). (Shape*T - Target) → Each column has errors that are from 3 different student T distributions. I would like to be able to write my 3 likelihoods as below.
...... # Expected value of outcome mu = tt.dot(shape, T) # Likelihood (sampling distribution) of observations Y_obs_x = pm.StudentT("Y_obs_x",nu=nu_x , mu=mu[:, 0], sigma=sigma_x, observed=target[:, 0]) Y_obs_y = pm.StudentT("Y_obs_y",nu=nu_y , mu=mu[:, 1], sigma=sigma_y, observed=target[:, 1]) Y_obs_z = pm.StudentT("Y_obs_z",nu=nu_z , mu=mu[:, 2], sigma=sigma_z, observed=target[:, 2])
However I can’t do this since slicing tensors is not allowed for mu. What can I do?