import pymc as pm
import numpy as np
import matplotlib.pyplot as plt

# Generate fake data
data_scores = np.random.normal(0.5, 0.1, size=50)


mu = pm.Uniform("mu", lower=0, upper=1)
tau = pm.Uniform("tau", lower=0, upper=500)
scores = pm.Normal("score", mu=mu, tau=tau, observed=True, value=data_scores)
model = pm.Model([scores, mu, tau])
mcmc = pm.MCMC(model)
mcmc.sample(40000, 10000, 1)

mu_samples = mcmc.trace('mu')[:]

plt.hist(mu_samples, bins=30)
plt.show()