Why some intermediate variables are not saved in the trace?

Questions v5
1

I have the following code,

with pm.Model(coords=coords) as baseModel:

    # input data
    xx_ = pm.MutableData("xx_", xx)
    yy_ = pm.MutableData("yy_", yy_obs)
    sm_ = pm.MutableData("sm_", sm_obs)

    #priors
    sigma_obs = 0.1

    A_ = pm.Uniform('A_', 0, 1)
    B_ = pm.Uniform('B_', 0, 1)
    C_ = pm.Uniform('C_', 0, 1)
    D_ = pm.Uniform('D_', 0, 1)
    E_ = pm.Uniform('E_', 0, 1)
    F_ = pm.Uniform('F_', 0, 1)

    x1_ = A_ + B_*xx_
    x2_ = C_ + D_*xx_
    x3_ = E_ + F_*xx_

    def f(x1_ = x1_, x2_ = x2_, x3_ = x3_, sm_ = sm_):
        out = T11(x1_, x2_, x3_ ,sm_)
        return out

    function_pm = pm.Deterministic('s0f', f())

    obs = pm.Normal('obs', mu=function_pm, sigma=sigma_obs, observed=yy_)

where T11 is an external function. As is, the model only samples the variables, Sampling: [A_, B_, C_, D_, E_, F_, obs]. Why not x1, x2 and x3? I know I can recompute them, but I want to follow the scheme proposed here to use the posteriors of A… F once calibrated to do further inference, and the values of A…F are not stored in the posterior predictive.

2

x1, x2, and x3 need to be set to Deterministics if you want them saved in the trace.

x1_ = pm.Deterministic('x1', A_+B_*xx_)
3

Thank you!