如何在PyMC 2.3.7的Python语言中计算边际似然？

import pymc as pm
import numpy as np

# 创建一个模型对象
model = pm.Model()

# 定义参数
with model:
    mean = pm.Uniform('mean', lower=0, upper=10)
    std_dev = pm.Uniform('std_dev', lower=0, upper=10)
    data = pm.Normal('data', mu=mean, sd=std_dev, observed=[1, 2, 3, 4, 5])

# 使用MCMC方法进行采样
with model:
    trace = pm.sample(1000, tune=500, discard_tuned_samples=True)

# 计算边际似然
with model:
    marginal_likelihood = np.exp(model.logp(trace[-1]))
    print("边际似然为:", marginal_likelihood)