Currently, in your model there is no free parameter, in another word, there is nothing to be estimated. You are basically sampling from a MvNormal(mu0, V0).
To represent prior information in the model, you need to represent them as distribution, and let them interact with each other, for example, you can do:
with pm.Model() as Maher_model:
theta = pm.MvNormal('theta', mu=mu0, cov=V0, shape=6)
H0 = pm.Uniform('H0', 0, 1, shape=(4, 6))
g = tt.dot(H0, theta)
likelihood = pm.MvNormal('likelihood', mu=g, cov=sigma, shape=4, observed=g)
where the prior information about g is represented using theta and H0.