我有一个简单的分层模型,其中包含许多个人,我有来自正态分布的小样本。这些分布的均值也服从正态分布。
import numpy as np
n_individuals = 200
points_per_individual = 10
means = np.random.normal(30, 12, n_individuals)
y = np.random.normal(means, 1, (points_per_individual, n_individuals))
我想使用 PyMC3 从样本中计算模型参数。
import pymc3 as pm
import matplotlib.pyplot as plt
model = pm.Model()
with model:
model_means = pm.Normal('model_means', mu=35, sd=15)
y_obs = pm.Normal('y_obs', mu=model_means, sd=1, shape=n_individuals, observed=y)
trace = pm.sample(1000)
pm.traceplot(trace[100:], vars=['model_means'])
plt.show()
我期待的后验model_means
看起来像我原来的均值分布。但它似乎收敛到30
均值。如何从 pymc3 模型中恢复均值的原始标准差(在我的示例中为 12)?