thanks for your response. But I still am not able to calculate "mu." The issue is that I need to use the sigma_squared ('estimate_data_noise') which is defined from a separate function than the function which calculates 'mu'. Any idea how that could work? Thank you! ### YOUR SOLUTION HERE input_x = data[['GrLivArea','YearBuilt']].head(100).values output_y = data['SalePrice'].head(100).values lambda_param = .1 ##calculation for mu def calculate_map_coefficients(input_x, output_y, lambda_param, sigma_squared): xT = np.transpose(input_x) lambda_mul_singma = lambda_param * sigma_squared; fist_inv = np.linalg.inv(lambda_mul_singma + np.matmul(xT, input_x)) mu = np.matmul(np.matmul(fist_inv, xT), output_y) return mu ##calculation for sigma_squared def estimate_data_noise(input_x, output_y, weights): n = len(input_x[:, 0]) d = len(input_x[0, :]) diff = [] for element in range(len(output_y)): print(element) diff.append((output_y[element] - input[element] @ weights) ** 2) return sum(diff) / (n - d)