Setting boundaries for prior distributions

Questions
7

thank for all the guidance. Just one small question, I passed by this question where one of the comments said the following:

"Very beginner user here, so consider all I write with rightful suspicion.
The -inf in the evaluation suggests you have some strange parametrization going on, but I honestly could not find anything wrong with your model – but wait for more expert user to chime in.
Something you could try is to get rid of the sigmas parameter completely. Just fix the sigma of y_obs to 1 and see if you still have an error – since the mu parameter is not constrained it should not be the one causing the error.
However, you should be able to tell the sampler where to start. You may rewrite your sampling line as:

trace = pm.sample(2000, tune=1000, start={'alpha': np.array([0.]), 'tau': np.array([0.]), 'sigma_c': np.array([0.]), 'sigma_t': np.array([0.])})

Naturally, you are free to change the starting values as you please – withing the boundaries of acceptability for each parameter.

Could this work for my situation? (Link to question:Error with model)