Maximum Likelihood Estimation v2

Continuation of https://matchmaticians.com/questions/setkfa .
After receive answer I'm still lost, since I'm not sure what pmodel is(see comments section there).
What exactly pmodel is? Why do we work with pmodel, but not pdata, maximizing its parameters, if it's a family(I udnerstand it as an array for example) of all possible distributions, so I'm little confused here.
If you can explain more widely please.
P.S. Question specifically for Mathe

  • Mathe Mathe
    +1

    I'll give an answer in a minute.

    • Mathe Mathe
      0

      It looks like I can't upload a file, but I can share a link, I believe.

    • doesnt matter i think

1 Answer

Solution can be found in

https://drive.google.com/file/d/1OU0l9LXxm2dNXtyPipCWbw0Ho69Be4dy/view?usp=drive_link

  • grant access please

    • Mathe Mathe
      0

      Anyone with the link can view the file.

    • now yes, previously i couldn't

    • Ok, thx, tipped

    • @Mathe so to sum up, we have some unknown distribution pdata and some set of samples from it, then we choose somehow(I guess based on some basic conclusions) some distribution. And for this distribution based on our samples we will estimate it's parameters by maximizing it's likelihood function, right? For example if I have normal distribution, it's likelihood function I also know, I can't find it's partial derivatives for mean and variance and solve for 0, than paste all samples and that's it?

    • I meant I CAN here "I can't find it's partial derivatives for mean and variance and solve for 0, than paste all samples and that's it? "

  • Mathe Mathe
    0

    Yes, that is correct. You take partial derivatives and solve for zero.

    • Ok, thanks again for great explanation

  • @Mathe Hi, would you like to answer to similar question about Bayesian statistics for the same amount?

    • Mathe Mathe
      0

      Yes! Share the question.

    • @Mathe https://matchmaticians.com/questions/qse0ka

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