#Markov inequality explain??
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Markov inequality implies the Chebyshev inequality, for one, very useful tool in probablity theory
the bottom inequality follows from monotonicity of integral
there's nothing special to this proof, just definitions
revise them
How come is X 1x>=lbd >= lbd 1x>=lbd??
X is a positive random variable, it can be a lot of high values
and you tell me that even so it is always bigger than any lambda chosen?
Can't I just pick a high enough lbd to get over the higehst value of X?
what are you even asking
Literally that
aL
Set of all points in the sample space satisfying X(x) >= lbd
Yes
hence we only care about those trajectories on which X(w) >= lambda
Yes
and that's all
??
that's why the inequality holds
you are multiplying by 1
I mistook the X below as a small x
$$ X(\omega) \geqslant \lambda \Rightarrow I_{{X\geqslant \lambda}}(\omega) = 1 $$
No need for further explanations guys
aL
I get it now
Basically $\forall \omega \in \Omega, X(\omega) \geq X(\omega)1_{X \geq \lambda}(\omega) \geq \lambda 1_{X \geq \lambda}(\omega)$ you can check that’s it’s true and so using the property of the expectation you have the result
😑 rotoR
Ah okay good
+close