测试通过环境：

pymc3==3.11.0

python=3.8

测试代码：

import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pymc3 as pm
RANDOM_SEED = 8927
np.random.seed(RANDOM_SEED)
az.style.use("arviz-darkgrid")
# True parameter values
alpha, sigma = 1, 1
beta = [1, 2.5]
 
# Size of dataset
size = 100
 
# Predictor variable
X1 = np.random.randn(size)
X2 = np.random.randn(size) * 0.2
 
# Simulate outcome variable
Y = alpha + beta[0] * X1 + beta[1] * X2 + np.random.randn(size) * sigma
fig, axes = plt.subplots(1, 2, sharex=True, figsize=(10, 4))
axes[0].scatter(X1, Y, alpha=0.6)
axes[1].scatter(X2, Y, alpha=0.6)
axes[0].set_ylabel("Y")
axes[0].set_xlabel("X1")
axes[1].set_xlabel("X2")
basic_model = pm.Model()
if __name__ == '__main__':
 with basic_model:
 
    # Priors for unknown model parameters
    alpha = pm.Normal("alpha", mu=0, sigma=10)
    beta = pm.Normal("beta", mu=0, sigma=10, shape=2)
    sigma = pm.HalfNormal("sigma", sigma=1)
 
    # Expected value of outcome
    mu = alpha + beta[0] * X1 + beta[1] * X2
 
    # Likelihood (sampling distribution) of observations
    Y_obs = pm.Normal("Y_obs", mu=mu, sigma=sigma, observed=Y)
 
# instantiate sampler
    step = pm.Slice()
 
    # draw 5000 posterior samples
    trace = pm.sample(5000, step=step, return_inferencedata=False)
    az.plot_trace(trace)
    plt.show()

测试结果：