Closed erineidschun closed 5 years ago
Hi @erineidschun . Yeah. It really depends entirely on how you choose to write the function neg_log_lik
. You might not need them at all. The point of the args
keyword argument in minimize
is to allow the programmer to send extra arguments to the function to be optimized. In your case, that's neg_log_lik
. So, if your neg_log_lik
function takes in extra arguments besides theta
, then you need it. Otherwise, you don't.
I am confused as to what exactly mle_args should be when we are running scipy.optimize.minimize. Both of the choices below give me compile errors.
1) mle_args = df 2) mle_args = df.TPOP.values, df.OPOP.values, df.PGRW.values, df.NGRW.values, df.OCTY.values, df.ELD.values, df.PINC.values, df.LNHDD.values, df.FFRAC.values, df.LANDV.values
theta = opt.minimize(neg_log_lik, theta0, args = (mle_args))
Can you explain in general what mle_args is supposed to be? I am not understanding it from our past notes/hw 5. Thanks!