def plot_mean_bootstrap_exponential_readme():
X = np.random.exponential(7, 4)
classical_samples = [np.mean(resample(X)) for _ in range(10000)]
posterior_samples = mean(X, 10000)
l, r = highest_density_interval(posterior_samples)
classical_l, classical_r = highest_density_interval(classical_samples)
plt.subplot(2, 1, 1)
plt.title('Bayesian Bootstrap of mean')
sns.distplot(posterior_samples, label='Bayesian Bootstrap Samples')
plt.plot([l, r], [0, 0], linewidth=5.0, marker='o', label='95% HDI')
plt.xlim(-1, 18)
plt.legend()
plt.subplot(2, 1, 2)
plt.title('Classical Bootstrap of mean')
sns.distplot(classical_samples, label='Classical Bootstrap Samples')
plt.plot([classical_l, classical_r], [0, 0], linewidth=5.0, marker='o', label='95% HDI')
plt.xlim(-1, 18)
plt.legend()
plt.savefig('readme_exponential.png', bbox_inches='tight')
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