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%matplotlib inline
from IPython.core.pylabtools import figsize
import numpy as np
from matplotlib import pyplot as plt
import matplotlib
matplotlib.rc(‘font’, family=’Malgun Gothic’)
 
figsize(11, 9)
 
import scipy.stats as stats
 
dist = stats.beta
n_trials = [0, 1, 2, 3, 4, 5, 8, 15, 50, 500]
data = stats.bernoulli.rvs(0.5, size=n_trials[-1])
x = np.linspace(0, 1, 100)
 
for k, N in enumerate(n_trials):
    sx = plt.subplot(len(n_trials) / 2, 2, k + 1)
    plt.xlabel(”$p$, 앞면의 확률”, fontsize=13) 

        if k in [0, len(n_trials) - 1] else None
    plt.setp(sx.get_yticklabels(), visible=False)
    heads = data[:N].sum()
    y = dist.pdf(x, 1 + heads, 1 + N - heads)
    plt.plot(x, y, label=“%d 번의 동전 던지기,\n앞면 %d 번 관측” % (N, heads))
    plt.fill_between(x, 0, y, color=”#348ABD”, alpha=0.4)
    plt.vlines(0.5, 0, 4, color=“k”, linestyles=”–”, lw=1)
 
    leg = plt.legend()
    leg.get_frame().set_alpha(0.4)
    plt.autoscale(tight=True)
 
plt.suptitle(“사후확률의 베이지안 업데이트” y=1.02,fontsize=14)
 
plt.tight_layout()

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