Editing Maximum likelihood estimation (section)

=== [[Gradient descent]] method ===
(Note: here it is a maximization problem, so the sign before gradient is flipped)

<math display="block">\eta_r\in \R^+</math>  that is small enough for convergence and <math>\mathbf{d}_r\left(\widehat{\theta}\right) = \nabla\ell\left(\widehat{\theta}_r;\mathbf{y}\right)</math>

Gradient descent method requires to calculate the gradient at the rth iteration, but no need to calculate the inverse of second-order derivative, i.e., the Hessian matrix. Therefore, it is computationally faster than Newton-Raphson method.