L08.8 Normal Random Variables

[@nadekang8198]

I have been to mathematical statistics course in college and also read many textbooks but none of them explain the function formula of standard normal distribution as good as Prof Tsitsiklis does. It's crystal clear. I watched the original recorded course in MIT classroom, but found this one on edX to be more detailed! Thank you very much!!!

[@abbmireille8806]

I rarely comment on utube videos , BUT this explanation is so clear ,straightforward and smooth .THANK YOU

[@adnanmohamed6517]

It's great how you discuss the insight a formula give and what does it actually mean. Some go heavily into deriving the formulas rigorously (which is good) but forget that proofs do not always explain the meaning behind a formula.

[@jordankotiadis1258]

Με το που πάτησα το βίντεο κατάλαβα πως είναι Έλληνας. Ευχαριστούμε δάσκαλε

[@newbie8051]

Great explanation, prof SNS sharma (iiitdmj) explained it well too, but I was bored so I started binge watching probability videos xD

[@nipunkothari5518]

Hey from IIITD😂

[@newbie8051]

@@nipunkothari5518 Ah, nice to see juniors from other IIIT's trying to comprehend PRP xD You too are interested in machine learning I guess ?

[@mehmetkaanyildiz18]

Thank you sir. quite understandable approach

[@thoalfeqarali6979]

Thank you Prof.

[@NaofumiShinomiya]

How to prove that the E[X] of symmetric functions is at the point of symmetry?

[@berke-ozgen]

If X has a normal distribution, graph will be bell shape as explained. Let's say the maximum point of graph will be located at x'. As the function is symmetric both sides of x' will include same density, required by function itself. When you take the integral of pdf from - infinity to x' and the integral of pdf from x' to the infinity, you will show that these areas are equal. Then x' is the symmetry point and equal to E[X]