I am reading the wikipedia article on ordered logit models. As I understand it, the model is specified by:
$\Pr(y \le k | \mathbf{x}) = \frac{1}{1 + e^{\mathbf{w} \cdot \mathbf{x} - \theta_k}}$
where $\theta_k$ are the thresholds $\theta_1 <\theta_2 < \dots < \theta_{K-1}$. (NB: The original wiki formula has $\theta_i$ appearing on the RHS. This does not seem meaningful to me so I changed it to $k$ instead.)
This implies that the probability that the observation $\mathbf{x}$ falls in the $k$-th class is
$\Pr(y = k | \mathbf{x}) = \Pr(y \le k | \mathbf{x}) - \Pr(y \le k - 1 | \mathbf{x})$.
The article then goes on to say that to fit the model you need to find the coefficients $\mathbf{w}$ and the thresholds $\theta_k$. So far so good, except it does not tell you how to implement a procedure to find these.
My question is, how do you determine the best set of coefficients and thresholds? Is it with respect to a maximum likelihood estimator for the predicted probability for the actual class of the response?
Note that I am not interested in packages/software that can fit the ordered logit model.