L08.6 Exponential Random Variables

[@durgeshmishra4005]

"The time until a metorite falls on your house" 😆

[@shriram5494]

The playlist went to the next video immediately after that line lol. Caught me off guard. I came back here just to check the comments 😂

[@xy1112]

got to admit that the same topic of this part is much clearly demonstrated by MIT than Harvard.

[@まつまつ-x1f]

An awesome and concise introduction to exponential random variable!

[@meseretabera6350]

I love his sense of humor though, time until a meteorite falls on your house :)

[@toughmuon4003]

Will the lambda be too small for the meteorite situation or receiving a response mail for a job application ?

[@xbyang6988]

yes

[@ZorroNumber1]

Not if you measure time by a bigger unit than years, say millenniums

[@xbyang6988]

@@ZorroNumber1 Thank you, your answer gives me a deeper understanding of this knowledge

[@TeraHertze]

thank you

[@cemisgezeksakini406]

Thanks for the great video. I have a question: Lets say we have a RV X. Integrating the pdf from -inf to a particular point "a" gives us P(X a) ? So 1-F_X(a) = P(X>a) = integrating the pdf from a to +inf ?

[@damandeepsingh7932]

Yes. That seems correct.

[@abir95571]

in case of continuous R.V P(x>a) = P(x>=a) ... if you recall P(x=a) = 0 and P(x>=a) = P(x=a)+P(x>a). The strictly greater than or equal to sign is critical for discrete R.V not so much for contiguous.

[@entertainmentking1759]

what if p(a

[@archilkumar]

That is 1-p(a>=15) = 1 - e^(-λa)

[@NaofumiShinomiya]

​@@archilkumar u got it