相关疑难解决方法(0)

这是MWE我正在使用的更大的代码.基本上,它针对位于特定阈值以下的所有值对KDE(内核密度估计)执行蒙特卡洛积分(在该问题上建议积分方法BTW:积分2D核密度估计).

import numpy as np
from scipy import stats
import time

# Generate some random two-dimensional data:
def measure(n):
    "Measurement model, return two coupled measurements."
    m1 = np.random.normal(size=n)
    m2 = np.random.normal(scale=0.5, size=n)
    return m1+m2, m1-m2

# Get data.
m1, m2 = measure(20000)
# Define limits.
xmin = m1.min()
xmax = m1.max()
ymin = m2.min()
ymax = m2.max()

# Perform a kernel density estimate on the data.
x, y = np.mgrid[xmin:xmax:100j, ymin:ymax:100j]
values = np.vstack([m1, …