详解Python如何模拟伯努利试验和二项分布

from scipy import stats
import numpy as np
p = 0.5
# 生成冻结分布函数
bernoulliDist = stats.bernoulli(p) 

# 模拟 27 次伯努利实验
trails = bernoulliDist.rvs(27) 

# 查看结果
trails

import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
 
Ps = [0.5, 0.6, 0.7]
Ns = [20, 20, 20]
colors = ['blue', 'green', 'red']
 
# 模拟试验绘制图形
for p,n, c in zip(Ps, Ns, colors):
  binomDist = stats.binom(n, p)
  P_k = binomDist.pmf(np.arange(n + 1))
   
  label='p={},n={}'.format(p, n)
  plt.plot(P_k, '--',marker='o', label=label, ms=5)
   
plt.xlabel('X')
plt.ylabel('P(X)')
plt.legend()
 
plt.show()