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在 PyMC3 ( https://docs.pymc.io/notebooks/GLM-hierarchical.html ) 的这个例子中，用一维样本估计后验分布（数据集中的样本只有一个特征）。

    # Hyperpriors for group nodes
    mu_a = pm.Normal('mu_a', mu=0., sigma=100)
    sigma_a = pm.HalfNormal('sigma_a', 5.)
    mu_b = pm.Normal('mu_b', mu=0., sigma=100)
    sigma_b = pm.HalfNormal('sigma_b', 5.)

    # Intercept for each county, distributed around group mean mu_a
    # Above we just set mu and sd to a fixed value while here we
    # plug in a common group distribution for all a and b (which are
    # vectors of length n_counties).
    a = pm.Normal('a', mu=mu_a, sigma=sigma_a, shape=n_counties)
    # Intercept for each county, distributed around group mean mu_a
    b = pm.Normal('b', mu=mu_b, sigma=sigma_b, shape=n_counties)

    # Model error
    eps = pm.HalfCauchy('eps', 5.)

    radon_est = a[county_idx] + b[county_idx]*data.floor.values

    # Data likelihood
    radon_like = pm.Normal('radon_like', mu=radon_est,
                           sigma=eps, observed=data.log_radon)

如果我的数据集样本具有N 维特征，如何概括？我试图用

radon_est = a[county_idx] + np.dot(b[county_idx],data)

但它不起作用

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