Scipyで標本分布を計算する(離散分布) ref: http://qiita.com/yk-tanigawa/items/0e4bd9480216c7a84106

file0.txt

from scipy import stats
help(stats.rv_discrete)

file1.txt

from scipy import stats
xk = np.arange(7)
pk = (0.1, 0.2, 0.3, 0.1, 0.1, 0.0, 0.2)
custm = stats.rv_discrete(name='custm', values=(xk, pk))

file2.txt

import matplotlib.pyplot as plt
fig, ax = plt.subplots(1, 1)
ax.plot(xk, custm.pmf(xk), 'ro', ms=12, mec='r')
ax.vlines(xk, 0, custm.pmf(xk), colors='r', lw=4)
plt.show()

file3.txt

import numpy as np
# np.random.seed(0)
sampled_rvs = custm.rvs(size=1000)

file4.txt

f = np.histogram(sampled_rvs, bins = np.arange(7 + 1), density=True)[0]

file5.txt

import numpy as np
from scipy import stats

def sampling_dist(p, size, seed = 0):
    xk = np.arange(p.size)
    pk = p.ravel()
    true_dist = stats.rv_discrete(name='true_dist', values=(xk, pk))

    np.random.seed(seed)
    sampled_rvs = true_dist.rvs(size=size)

    f = np.histogram(sampled_rvs, bins = np.arange(p.size + 1), density=True)[0]
    f.reshape(p.shape)

    return(f.reshape(p.shape))

p = np.array([[0.10,0.10],[0.10,0.15],[0.15,0.10],[0.10,0.20]])
p_est = sampling_dist(p, 1000)
print(p)
print(p_est)