Bug: Normal distribution throws errors when using shared variables

[benyi1981]

I am having trouble implementing Normal distribution with mu that is a shared variable. See below example code:

Please provide a minimal, self-contained, and reproducible example.

product_mu_shared = shared(0.0)
product_sd = 0.1
total_quantity_shared = shared(np.array([0.0, 0.1, 0.2]))

with pm.Model() as quantity_model:    
    
    # Priors
    a = pm.Lognormal('a',mu=product_mu_shared, sd=product_sd)
    b = pm.Normal('b',mu=0.0, sd=product_sd)
    d = pm.Normal('d', mu=product_mu_shared, sd=product_sd)


    # Data likelihood
    vol = pm.Normal('vol',mu= a+b+d, observed=total_quantity_shared)
        
    # Inference
    trace = pm.sample(10,tune=10)

Please provide the full traceback.

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-6-6131815c61f7> in <module>()
      4     a = pm.Lognormal('a',mu=product_mu_shared, sd=product_sd)
      5     b = pm.Normal('b',mu=0.0, sd=product_sd)
----> 6     d = pm.Normal('d', mu=product_mu_shared, sd=product_sd)
      7 
      8 

C:\ProgramData\Anaconda3\lib\site-packages\pymc3\distributions\distribution.py in __new__(cls, name, *args, **kwargs)
     40             total_size = kwargs.pop('total_size', None)
     41             dist = cls.dist(*args, **kwargs)
---> 42             return model.Var(name, dist, data, total_size)
     43         else:
     44             raise TypeError("Name needs to be a string but got: {}".format(name))

C:\ProgramData\Anaconda3\lib\site-packages\pymc3\model.py in Var(self, name, dist, data, total_size)
    806                 with self:
    807                     var = FreeRV(name=name, distribution=dist,
--> 808                                  total_size=total_size, model=self)
    809                 self.free_RVs.append(var)
    810             else:

C:\ProgramData\Anaconda3\lib\site-packages\pymc3\model.py in __init__(self, type, owner, index, name, distribution, total_size, model)
   1205             self.distribution = distribution
   1206             self.tag.test_value = np.ones(
-> 1207                 distribution.shape, distribution.dtype) * distribution.default()
   1208             self.logp_elemwiset = distribution.logp(self)
   1209             # The logp might need scaling in minibatches.

C:\ProgramData\Anaconda3\lib\site-packages\pymc3\distributions\distribution.py in default(self)
     65 
     66     def default(self):
---> 67         return np.asarray(self.get_test_val(self.testval, self.defaults), self.dtype)
     68 
     69     def get_test_val(self, val, defaults):

C:\ProgramData\Anaconda3\lib\site-packages\pymc3\distributions\distribution.py in get_test_val(self, val, defaults)
     70         if val is None:
     71             for v in defaults:
---> 72                 if hasattr(self, v) and np.all(np.isfinite(self.getattr_value(v))):
     73                     return self.getattr_value(v)
     74         else:

TypeError: ufunc 'isfinite' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''```

**Please provide any additional information below.**


## Versions and main components

* PyMC3 Version: 3.5
* Theano Version: 1.0.2
* Python Version: 3.6
* Operating system: Windows 10
* How did you install PyMC3: (conda/pip) conda-forge

[junpenglao]

Background discussion see discourse post

The error is that PyMC3 is trying to evaluate whether the default value is finite (for Gaussian is the mean), however np.isfinite cannot evaluate theano.shared type.
Current workaround is assigning a test value to Normal:

d = pm.Normal('d', mu=product_mu_shared, sd=product_sd, testval=0.)

[rpgoldman]

this is really a question, and I'm inclined to close it, but should we do something to make this error easier to recognize? Like maybe trap the TypeError and add a suggestion of adding a test value?