How to account for multiple betas in example documentation?

Questions v5
2

It depends on whether each coeffcient was a separate parameter or if you have a parameter array (e.g., betas=pm.Normal("betas, dims="obs_id")). If they are separate, you could just do something along these lines:

mu_pp = (
        post["a"] + 
        post["b1"] * xr.DataArray(predictor_1, dims=["obs_id"] +
        post["b2"] * xr.DataArray(predictor_2, dims=["obs_id"] +
        post["b3"] * xr.DataArray(predictor_3, dims=["obs_id"]
        )

Following that example, however, it would be easier to just save the predicted means like this:

with pm.Model() as model_1:
    a = pm.Normal("a", 0.0, 0.5)
    b = pm.Normal("b", 0.0, 1.0)

    mu = pm.Deterministic("mu", a + b * predictor_scaled)
    sigma = pm.Exponential("sigma", 1.0)

    pm.Normal("obs", mu=mu, sigma=sigma, observed=outcome_scaled)
    idata = pm.sample_prior_predictive(samples=50, random_seed=rng)

And then you should be able to do something like this:

plt.scatter(predictor_scaled, post["mu"].mean(("chain", "draw")))

Not 100% on any of that code running as-is, but should get you started. If not, feel free to ask.