t_bounds = bounds
                opt_lu = [X1[idx], X1[idx] + newX[m - 1, idx]]

    # recover distribution parameters at optimum
    alpha = (opt_lu[1] - opt_lu[0]) / np.diff(opt_bounds)
    mu = opt_lu[0] - opt_bounds[0] * alpha
    beta = opt_beta

    # calculate the distribution&#39;s standard deviation from alpha and beta
    sig = np.sqrt((alpha ** 2) * gamma(3 / beta) / gamma(1 / beta))

    return mu, sig, alpha, beta


def yulewalk(order, F, M):
    &#34;&#34;&#34;Recursive filter design using a 