Value error in chains for a gaussian process

Questions v5
3

Thanks, I’ll post it in the github repo asap. Let me clarify for future users (myself included) the changes:

with pm.Model() as d_model:
    # Specify the covariance function.
    vert = 1*pm.HalfNormal("vert", sigma=1)
    l = pm.HalfNormal(name='l', sigma=1)
    cov_func = vert**2 * pm.gp.cov.Matern32(1, ls=l)

    # Specify the GP. The default mean function is zero.
    #gp = pm.gp.Marginal(mean_init, cov_func=cov_func)
    gp = pm.gp.Marginal(cov_func=cov_func)

    # Place a GP prior over the function f and do the noise:
    sigma = pm.HalfNormal("sigma", sigma=1)
    y_ = gp.marginal_likelihood("y_", X=X, y=y.flatten(), noise=sigma)

    # MCMC:
    trace = pm.sample(1000, chains=3, tune=1000, target_accept=0.99)

X_new = np.linspace(-1.5, 1.5, 100)
X_new = X_new.reshape(-1,1)
with d_model:
    f_p = gp.conditional("f_p", X_new)
    #Important to add var_names in the next one:!!
    pred_samples = pm.sample_posterior_predictive(trace, var_names=["f_p"])