Maximum Likelihood Estimation

Find a maximum likelihood estimator by hand.
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// MLE second example
• Maximum Likelihood Est...
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[@allenrahrooh7140]

you would have to verify it is a maximum by taking the second partial derivative of the likelihood function and if its negative it is a maximum

[@RollingcoleW]

Having his math content is more helpful than you know! I am not studying for something related to this, but it is great to have as review/interest.

[@zed1658]

This was the best video ever! You are a life saver

[@singkong7341]

I finally figure out what it is,love you so much!

[@BBtechnology]

When I just gave up on all this trash then u just rescued me 🥰God bless u

[@omphileashley3142]

you just saved my semester 😭

[@lola789ify1]

girl bless your heart!

[@gowthamvenkat3631]

Thank you so much, you saved my final!!!!!!!!

[@kneazleje]

you're a lifesaver

[@shafiqshams3065]

I simply love your accent

[@arashsadeghibablan2132]

AMAZING !

[@eea2375]

Ur the real MVP

[@ujjwalroy8835]

It is a pdf of weibull distribution

[@mirrorball1]

youre so smart

[@tszlome3474]

cute and useful, gonna watch more video in the channel

[@mathetal]

thank u :^)

[@MrKingoverall]

I love you !!!!!

[@nashnote1651]

thanks alot.....
:)

[@armeidiandanny0522]

where is come from beta^n and delta^n ??

[@meseretwubetu7826]

what about BETA

[@vigdad58143]

Why dont you take the summation of the other x in the problem?

[@kobebucket]

When you multiplied all the x's in the e^ßx^(Delta)) portion, why is it not e^-nß∑x^(delta) but just e^ß∑x^(delta)? Wouldn't we have to multiply ß n times too?

[@mathetal]

What you're multiplying is (e^Bx)*(e^Bx)*.... n times. So for example (e^Bx)*(e^Bx) = e^(Bx+Bx) = e^(B sigma x)

[@chumachuma5296]

you such a darling