Python, Monte Carlo, Monte Carlo Simulation, Importance Sampling, Machine Learning.
sigma_target) for _ in range(3000)], label="distribution $p(x)$") sns.distplot([np.random.normal(mu_appro, sigma_appro) for _ in range(3000)], label="distribution $q(x)$") plt.title("Distributions", size=16) plt.legend() # value s = 0 for i in range(n): # draw a sample x_i =