如何正确 plot pdf 的测试版 function 在 Z10EA590D34CD751CFF2CE4D34

Question

I am trying to fit a beta distribution to some data, and then plot how well the beta distribution fits the data. But the output looks really weird and incorrect.

import scipy.stats as stats 
import matplotlib.pyplot as plt

x = np.array([0.9999999 , 0.9602287 , 0.8823198 , 0.83825594, 0.92847216,
       0.9632976 , 0.90275735, 0.8383094 , 0.9826664 , 0.9141795 ,
       0.88799196, 0.9272752 , 0.94456017, 0.90466917, 0.8905505 ,
       0.95424247, 0.781545  , 0.9489085 , 0.9578988 , 0.8644015 ])
beta_params = stats.beta.fit(x)
print(beta_params)
#(3.243900357315478, 1.5909897101396109, 0.7270083219563888, 0.27811444901271615
 
beta_pdf = stats.beta.pdf(x, beta_params[0], beta_params[1], beta_params[2], beta_params[3])

print(beta_pdf)
#[2.70181543 6.8442073  4.98204632 2.82445508 6.76055614 6.75910611
 #5.90419012 2.82696622 5.58521916 6.34096675 5.2508072  6.73212694
 #6.98854653 5.98225724 5.36937625 6.9519977  0.67812362 6.99116729
 #6.89484982 4.10113147]

plt.plot(x, beta_pdf)

Answer 1

I'm not a statistician, but looking at your code I see that x is unordered.

Does sorting x before fit helps you?

x = np.sort(x)
beta_params = stats.beta.fit(x)

Doing so, you'd get this: