Monte Carlo, Markov Chains, Metropolis Hastings.
samples = np.append(samples, samples_t) figure, ax = plt.subplots(1,2) ax[0].hist(samples) ax[0].set_title('Histogram of 10k samples') ax[1].plot(samples) ax[1].set_title('10k Random samples') plt.show() This process is also called ‘random walk’, that the ending distribution is not always the same