如何在pymc3中计算道上GP的对数后验

import pymc3 as pm
import numpy as np

X = np.array([...])  # 输入数据
Y = np.array([...])  # 输出数据

with pm.Model() as model:
    # 定义高斯过程的超参数
    lengthscale = pm.Gamma('lengthscale', alpha=1, beta=1)
    variance = pm.HalfCauchy('variance', beta=1)
    
    # 定义高斯过程的协方差函数
    cov_func = variance * pm.gp.cov.ExpQuad(1, ls=lengthscale)
    
    # 定义高斯过程
    gp = pm.gp.Marginal(cov_func=cov_func)
    
    # 定义高斯过程的先验
    y = gp.marginal_likelihood('y', X=X[:, np.newaxis], y=Y, noise=0)

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

with model:
    # 获取对数后验
    logp = model.logp(trace)