我正在试验高斯分布及其可能性。为了计算出最大似然,我区分了mu(期望)和 sigma(均值)的似然,它们分别等于 data.mean() 和 data.std()
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.mlab as mlab
import math
from scipy.stats import norm
def calculate_likelihood(x, mu, sigma):
n = len(x)
likelihood = n/2.0 * np.log(2 * np.pi) + n/2.0 * math.log(sigma **2 ) + 1/(2*sigma**2) * sum([(x_i - mu)**2 for x_i in x ])
return likelihood
def estimate_gaussian_parameters_from_data(data):
return data.mean(), data.std()
def main():
mu = 0
sigma = 2
x_values = np.linspace(mu - 3*sigma, mu + 3*sigma, 1000)
y_values_1 = mlab.normpdf(x_values, mu, sigma)
estimated_mu, estimated_sigma = estimate_gaussian_parameters_from_data(y_values_1)
if (__name__ == "__main__"):
main()
我预计estimated_mu和estimated_sigma应该大约等于mu和sigma,但事实并非如此。而不是 0 和 2 我得到 0.083 和 0.069。我理解有什么不对吗?