Markov Chains, Python.
scale=2, size=100) # Initialize parameters mu_current = 0 # initial guess for μ sigma = 1 # known standard deviation n_samples = 20000 # Store the samples samples = [] for i in range(n_samples): # Propose new candidate from a symmetrical distribution mu_proposal = np.random.normal(loc=mu_current,