如何在pymc中实现简单蒙特卡罗函数

import pymc3 as pm
import numpy as np

with pm.Model() as model:
    # 定义参数
    theta = pm.Uniform('theta', lower=0, upper=1)
    
    # 定义观测数据
    data = np.array([0, 1, 0, 0, 1, 1, 0, 1])
    
    # 定义似然函数
    likelihood = pm.Bernoulli('likelihood', p=theta, observed=data)

with model:
    # 使用MCMC方法进行推断
    trace = pm.sample(1000, tune=1000)

# 获取参数估计值
theta_samples = trace['theta']

# 打印参数估计值的统计信息
print(pm.summary(trace))

# 绘制参数估计值的分布图
pm.plot_posterior(trace)