Unique solution for probabilistic PCA

Questions
27

Yes, I specified total_size.
The model code is like so:

import theano.tensor as tt
obs=np.array([d1,d2[0],d3[0],d4[0], yd[0]]).T
data_shape=obs.shape
batch_size=64
if batch_size:
    obs=pm.Minibatch(obs, batch_size)
else:
    batch_size=data_shape[0]
    
with pm.Model() as model1:
    s = pm.HalfCauchy('s', 1., shape=5)
    l_std=pm.HalfCauchy('l_std',1.)
    w = pm.Normal('w',0,1., shape=(5,2), testval=np.random.randn(5,2))
    
    latent_ = pm.Normal('latent_',0.,1., shape=(batch_size,2), testval=np.random.randn(batch_size,2))
    latent = pm.Deterministic('latent', latent_*l_std)
    p = pm.Deterministic('p',tt.dot(latent, w.T))
    cov = pm.Deterministic('cov', l_std**2*tt.dot(w, w.T)+(s**2)*np.eye(5))
    sd=pm.Deterministic('sd', tt.sqrt(tt.diag(cov)))
    
    latent_like = pm.Normal('latent_like', mu=p, sd=s, observed=obs, total_size=data_shape if batch_size!=data_shape[0] else None)