Markov Chains, Python.
implementing the Metropolis-Hastings algorithm using numpy: import numpy as np # Function to calculate the likelihood def likelihood(mu, data): return np.prod(np.exp(- (data - mu)**2 / 2) / np.sqrt(2 * np.pi)) # Generate observed data np.random.seed(123) observed_data = np.random.normal(loc=5,