Poisson Distribution

In this lesson we derive the expectation, variance, and moment generating function of Poisson random variable.
The derivation of the Poisson PMF is provided at www.actuarialpa...

[@nivedakavoor6085]

The website is not working. It is showing a 404 error.

[@cristianistrying7866]

replace .html with .pdf

[@anuragkumar8852]

Very easy to understand
Thank u for making my concepts clear...
Love from India 🇮🇳

[@_edem19]

Doing the work most professors can’t/couldn’t do

[@lindokuhletshongolo6071]

thanks for making the concepts so clear... you should consider making more videos.. much love

[@koyalkardevanshu5146]

i have a quiz tmrw....... its 1:30am now......your videos are awesome

[@tumul1474]

i m in the exactly same situation....and it's 1:30

[@lakshithalathpandura6264]

Here 12.45a

[@abhinav6513]

3: 14 am 😅

[@mz_ppp]

also,in 17:01, what kind of expansion is that again? I dont understand it.

[@greenduck2606]

+Michael Zeng Taylor Series, the one he showed before too while proving sum of PMF = 1.

[@UsmanAli-hx7ef]

Please tell me about pdf of Poisson Distribution

[@pauljohncapote7541]

Still reviewing your video. I have my exam at 7:00pm

[@mz_ppp]

why it is equal to E[x] in 13:45 with the extra j????

[@Darthdeedee91]

+Michael Zeng it is recursive... the way he simplifies Sum(ke^(-lambda))/k! to equal lambda is the same to simplify Sum(je^(-lambda))/j! to equal lambda. He should have used E(k) and E(j) that is where the confusion happened . But anyways both E(k) and E(j) simplify to lambda.

[@bradleyshepard]

well done

[@dangtran2209]

very clearly!

[@IceColdH]

amazing

[@grdsyt]

great!