L18.2 The Markov Inequality
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- Опубліковано 7 жов 2024
- MIT RES.6-012 Introduction to Probability, Spring 2018
View the complete course: ocw.mit.edu/RE...
Instructor: John Tsitsiklis
License: Creative Commons BY-NC-SA
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this is really clear !! thank you mit
Holy moly! That was amazing! Thank you so much!!
woooooooooooowwwwwwwwwwwwww!!!! adbhutttttttt ......... amazing explanation. Taaliya bajti rehni chahiye. love u , u r my best friend.
MIT rocks!!
best course ever
A very clear explanation with examples in 10 minutes. Thank you!
Well explained and simple to understand. Thank you!
Thank you, professor!
No wonder I can't get into MIT
λεω σαν Ελληνας ακουγεται... και μετα κοιταω κατω στην περιγραφή και βγαινω σωστος :p Ευχαριστούμε!!!
χαχαχαχχχχαχαχα και εγω
brilliant
Efcharistó polý Professor Tsitsiklis
I believe there's a mistake at 9:45. That should be 2, not 1/2.
No, 1/2 is correct.
thank youuuuuuuu
this was awesome!
great video thanks
For exponential distribution isn’t the distribution function P(X=a) also e^(-a)?
True value is calculated for P(X=a)
@@thisistruth01 Thank you
Because the specification of the example he presented is exponential within lambda =1. Ηope this helps.
how did you calculate the expected value of the absolute of x?
The range of the absolute value of x is [0.4] and the probability density function is uniform. So use the expectation formula then you get E(X)=2 ((from 0 to 4, use f(|x|)=1/4 as a pdf)). This is what I understood..
what is E[X] here?
I do not understand how it works with small a. for example, if E(X) is 4, and I want the probability that x exceeds 2. according to this formula it is smaller than 4/2 which is 2?! I know that! I even know that the probability is
It’s hard to understand u
3:50