Using opt.minimize on discrete data about comp-econ-sp19 HOT 3 OPEN

Hi. Good questions. Could you elaborate a little more? If you're asking about "discrete optimization", we didn't cover any of this in class. If you're wondering about using MLE to estimate the parameters of discrete random variables, such estimation falls within the domain of "continuous optimization." Our usual estimation procedures should work for this. Though, there are a lot of times when an optimizer will fail for one reason or another. If you're trying to optimize a function of one variable, sometimes using scipy.optimize.minimize_scalar works better.

from comp-econ-sp19.

I'm wondering about using MLE to estimate parameters of discrete random variables. My probability mass function and neg log lik functions work correctly, but my opt.minimize is returning the initial guess, no matter what the initial guess is. How would I go about fixing this problem?

from comp-econ-sp19.

Ok. Have you tried scipy.optimize.minimize_scalar? Also, have you tried other optimization methods within minimize? Take a look at the method keyword argument: https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html

But try minimize_scalar first.

from comp-econ-sp19.