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lambda_1_samples = mcmc.trace(‘lambda_1’)[:]
lambda_2_samples = mcmc.trace(‘lambda_2’)[:]
tau_samples = mcmc.trace(‘tau’)[:]
 
figsize(14.5, 10)
# 표본의 히스토그램
 
ax = plt.subplot(311)
ax.set_autoscaley_on(False)
 
plt.hist(lambda_1_samples, histtype=’stepfilled’, bins=30, alpha=0.85,
         label=”$\lambda_1$의 사후확률분포”, color=”#A60628”, normed=True)
plt.legend(loc=“upper left”)
plt.title(r“모수 $\lambda_1,\;\lambda_2,\;\tau$의 사후확률분포”)
plt.xlim([15, 30])
plt.xlabel(”$\lambda_1$ 값”)
plt.ylabel(“밀도”, fontsize=13)
 
ax = plt.subplot(312)
ax.set_autoscaley_on(False)
plt.hist(lambda_2_samples, histtype=’stepfilled’, bins=30, alpha=0.85,
         label=”$\lambda_2$의 사후확률분포”, color=”#7A68A6”, normed=True)
plt.legend(loc=“upper left”)
plt.xlim([15, 30])
plt.xlabel(”$\lambda_2$ 값”)
plt.ylabel(“밀도”,fontsize=13)
 
plt.subplot(313)
w = 1.0 / tau_samples.shape[0] * np.ones_like(tau_samples)
plt.hist(tau_samples, bins=n_count_data, alpha=1, label=r”$\tau$의 사후확률분포”, color=”#467821”, weights=w, rwidth=2.)
plt.xticks(np.arange(n_count_data))
plt.legend(loc=“upper left”)
plt.ylim([0, .75])
plt.xlim([35, len(count_data) - 20])
plt.xlabel(r”$\tau$ (일수)”,fontsize=13)
plt.ylabel(“확률”,fontsize=13);

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