L18.3 The Chebyshev Inequality

MIT RES.6-012 Introduction to Probability, Spring 2018
View the complete course: ocw.mit.edu/RES-6-012S18
Instructor: John Tsitsiklis
License: Creative Commons BY-NC-SA
More information at ocw.mit.edu/terms
More courses at ocw.mit.edu

Пікірлер: 30

  • @Dabsyboii
    @Dabsyboii Жыл бұрын

    I am literally crying right now cause no matter how many times I wentt to my professor or looked on google or through my notes, I could never understand this. I am so glad i found you

  • @Ash-vu5vo

    @Ash-vu5vo

    Жыл бұрын

    You still don’t understand it unfortunately (see my comment).

  • @q44444q
    @q44444q4 жыл бұрын

    The best, most intuitive, most thoughtful presentation of elementary concentration inequalities I have ever witnessed. Thank you Professor Tsitsiklis. I know it is unlikely that you see these comments, but I hope you do. Your teaching has made a world of difference to students all over the world.

  • @holly_singh
    @holly_singh Жыл бұрын

    This is great. Thank you so much Prof. Tsitsiklis

  • @dccn
    @dccn5 жыл бұрын

    Thank you! The proof of markov & chebyshev inequality is easy to be understood

  • @ns4543
    @ns45432 жыл бұрын

    the best explanation of Chebyshev's inequality! thanks!

  • @imranq9241
    @imranq92412 жыл бұрын

    Wouldn't Markov be a better bound for distributions with very high variance?

  • @sandrasurendran5068
    @sandrasurendran50683 жыл бұрын

    Thank you so much.

  • @zbynekba
    @zbynekba4 жыл бұрын

    Amazing insight, interpretation, and intuition. Thanks

  • @ylast3756
    @ylast3756 Жыл бұрын

    Thank you Indian brother. The explanation was very clear.

  • @pallaviharishchandre3021
    @pallaviharishchandre30213 жыл бұрын

    Thank you so much

  • @mr.shanegao
    @mr.shanegao3 жыл бұрын

    brilliant ! Great !

  • @danielm572
    @danielm5724 жыл бұрын

    beautiful

  • @xiscc-41suhaninair94
    @xiscc-41suhaninair942 ай бұрын

    Love from Jadavpur university 🎉🎉

  • @souravsidana9631
    @souravsidana96315 жыл бұрын

    Good explanation

  • @bluejimmy168
    @bluejimmy1684 жыл бұрын

    Does anyone know why p( x - 1 >= a -1) = a - 1) is a true statement at 4:34 . How do we know that the statement is true if we dont know the distribution of the probability?

  • @dsafadsddfca

    @dsafadsddfca

    4 жыл бұрын

    the distribution of the probability is not necessary, | x - 1| >= x-1 ie all values for x where x-1 >= a-1 also means that for the same values of x, | x - 1| >= a-1 . However, for values of x where x-1 = a-1 . Which means there that | x - 1| will at the very least have the same amount of values >= a-1 hence p( x - 1 >= a -1) = a - 1)

  • @bluejimmy168

    @bluejimmy168

    4 жыл бұрын

    @@dsafadsddfca thanks.

  • @prateekpani9464

    @prateekpani9464

    4 жыл бұрын

    The inequality is clearer if we see it in terms of area....say y = x-1....now y >= a-1 correspond to a certain domain-set( S1 ) and |y| >= a-1 correspond to {y | S1 union {S2 = y | y

  • @q44444q

    @q44444q

    4 жыл бұрын

    A different explanation: 1) We know that x-1 >= a-1 implies that |x-1| >= a-1. Why? If x-1 >= 0, it's trivially true, as x-1 = |x-1|. If x-1 = 0 > a-1, so |x-1| > a-1. 2a) We know that |x-1| >= a-1 DOES NOT ALWAYS imply that x-1 >= a-1. Here is a counterexample. Let x = -2 and a = 1. Then |x-1| = 3 >= a-1 = 0, but x-1 = -3 is not greater than a-1 = 0. 2b) Note that if x is strictly positive, then these two cases are the same: there is no counterexample. 3) Therefore, we know that there zero (case 2b) or more (case 2a) x's for which |x-1| > a-1 and x-1 >/= a-1. This is the very definition of P(|x-1| > a-1) being greater than P(x-1 > a-1). To show this more rigorously, remember that the definition of |x| >= a is that -a

  • @comusic2023
    @comusic202311 ай бұрын

    Very helpful

  • @Chankeypathak
    @Chankeypathak5 жыл бұрын

    What is the real world usage of Chebyshev? Can someone give an example please.

  • @GerardoRodriguez-cw6rj

    @GerardoRodriguez-cw6rj

    5 жыл бұрын

    When the probability distribution of a data set is unknown, you can determine the probability that the random variable X falls within a certain range of values given its mean and standard deviation. By looking at normal distributions with different degrees of kurtosis, you'll see that the Chebyshev inequality matters as the same range will have different probabilities of occurring in the respective normal distributions.

  • @msahasarkar590

    @msahasarkar590

    2 ай бұрын

    Very often used in the study of barren plateaus of quantum neural networks and exponential concentration of kernel methods in quantum machine learning.

  • @Retinal

    @Retinal

    11 күн бұрын

    @@GerardoRodriguez-cw6rj Doesn't that make the second example counter-intuitive since x is stated to have an exponential distribution?

  • @xiscc-41suhaninair94
    @xiscc-41suhaninair942 ай бұрын

    Any west bengal students here?

  • @ramanpal3908

    @ramanpal3908

    2 ай бұрын

    Yes

  • @Ash-vu5vo
    @Ash-vu5vo Жыл бұрын

    This is what college tuition fees gets you - incomplete proofs from incompetent teachers. Meanwhile all the sheeple viewers are grateful thinking they understand (when they don’t). |x - m| >= c implies (x - m)^2 >= c^2, sure, that is easily seen, but we are talking about their probabilities here and this guy has just straight up claimed they are equal (and the sheeple have just accepted). The world is full of incompetent teachers who need to be held accountable - I’m simply doing my little part.

  • @lynny7868

    @lynny7868

    9 ай бұрын

    You can do it without calling out names and sounding condescending.

  • @tejasgurjar6450

    @tejasgurjar6450

    2 ай бұрын

    Do you know that the P(X) and P(X^2) are same?