Understanding the supremum of a r.v. with an example

probability theoryrandom variablessupremum-and-infimum

Mentioning the definition of Supremum offered in supremum and limsup of random variables , we have:
For each $\omega \in \Omega$,
$$(\sup_{n \in \mathbb{N}} X_n)(\omega) := \sup\{X_n(\omega):n \in \mathbb{N}\}$$

However, I'm really struggling on understanding the definition, therefore I tried to imagine an expressive example:

Event: throw a die.

$(X_n)=\frac1n$ when $\omega=1,2$;

$(X_n)=\frac1{2𝑛}$ when $\omega=3,4$;

$(X_n)=0$ when $\omega=5,6$.

As far as I understood, the supremum is $\frac1{𝑛}$ and for checking it I have to analyse the value of the sequence for each 𝜔. Therefore I can see that for $\omega=1,2$, the maximum value that the sequence take is $\frac1{𝑛}$, while for $\omega=3,4,5,6$ the value is lower then $\frac1{𝑛}$. Therefore $sup=\frac1{𝑛}$.

Could you tell me if I understood it well? Or could you provide expressful example?

And what is the corresponding Infimum of this sequence? Is it right that it is $0$ for $\omega=1,2,3,4,5,6$

Best Answer

I preassume that $0\notin\mathbb N$ and that you meant to say that $X_n=0$ if $\omega=5,6$.

Then in your example:

  • $(\sup X_n)(\omega)=\sup\{X_n(\omega)\mid n\in\mathbb N\}=\sup\{\frac1n\mid n\in\mathbb N\}=1$ if $\omega\in\{1,2\}$
  • $(\sup X_n)(\omega)=\sup\{X_n(\omega)\mid n\in\mathbb N\}=\sup\{\frac1{2n}\mid n\in\mathbb N\}=\frac12$ if $\omega\in\{3,4\}$
  • $(\sup X_n)(\omega)=\sup\{X_n(\omega)\mid n\in\mathbb N\}=\sup\{0\}=0$ if $\omega\in\{5,6\}$
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