Independant RV's and the Borel-Cantelli Lemmas

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The discussion revolves around understanding the Borel-Cantelli Lemmas in relation to the Law of Large Numbers. The user expresses confusion about how to handle a specific term (S) and its connection to strong convergence. They provide an example demonstrating strong convergence and seek guidance on proving that a certain probability equals one. The conversation highlights the need to show that the sum of probabilities related to specific events converges, ultimately leading to the conclusion that the probability is zero. The thread emphasizes the interplay between these mathematical concepts and the user's quest for clarity.
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The first part I can do no problem!

The second part... well I've really no idea what to do with that S term, I assume it has something to do with the strong law of large numbers? (w is an element in the set of outcomes)

Borel-Cantelli Lemmas

Law of Large Numbers

Does anyone know where to start?

Thanks

edit:

I have an exmple of strong convergence showing that

if P(w : lim Xn(w) = X(w) )

then this is identically equal to

P(Xn -> X) = 1 , almost surely

So I guess I just have to show that P(Xn -> X) = 1,

So show: P(Sn(w)/n -> -1) = 1?

Probably going the wrong way around it.. not sure how Borel Cantelli links to it.
 
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Firepanda said:
wvcuvp.png


The first part I can do no problem!

The second part... well I've really no idea what to do with that S term, I assume it has something to do with the strong law of large numbers? (w is an element in the set of outcomes)

Borel-Cantelli Lemmas

Law of Large Numbers

Does anyone know where to start?

Thanks

edit:

I have an exmple of strong convergence showing that

if P(w : lim Xn(w) = X(w) )

then this is identically equal to

P(Xn -> X) = 1 , almost surely

So I guess I just have to show that P(Xn -> X) = 1,

So show: P(Sn(w)/n -> -1) = 1?

Probably going the wrong way around it.. not sure how Borel Cantelli links to it.

It is probably simpler to re-write the problem a bit: let X_n = -1 + Y_n, where \Pr\{Y_n = 0 \} = 1 - \frac{1}{n^2}, \mbox{ and } \Pr \{Y_n = n^2 \} = \frac{1}{n^2}. Essentially, you want to show that \sum_{k=1}^n Y_k / n \rightarrow 0 w.p. 1. If A = \{ Y_n > 0 \mbox{ infinitely often } \}, can you show that Pr{A} = 0?

RGV
 
Hi RGV, you always answer my questions it seems! :)

So I need to show given the event En = Yn>0

that the sum from n=1 to inf of P(En) is finite (Borel Cantelli)

So this is the sum of n=1 to inf of 1/n2?

Which is pi^2/6, and so the probability is 0!

Correct?
 
Also any idea on the second part of this question?

v5ltep.png


It looks like the same structure..

Thanks!
 
bump..!
 
Question: A clock's minute hand has length 4 and its hour hand has length 3. What is the distance between the tips at the moment when it is increasing most rapidly?(Putnam Exam Question) Answer: Making assumption that both the hands moves at constant angular velocities, the answer is ## \sqrt{7} .## But don't you think this assumption is somewhat doubtful and wrong?

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