Here is a github issue and solution that I saw the other day. I think it’s a nice pattern.

def generate_model(values={'mu': true_param, 'm': None}):
#prior
mu = pymc.Uniform("mu", lower=-10, upper=10, value=values['mu'],
observed=(values['mu'] is not None))
# likelihood function
m = pymc.Normal("m", mu=mu, tau=tau, value=values['m'],
observed=(values['m'] is not None))
return locals()

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Tagged as MCMC, pymc, python