B1.13

Probability and Measure | Part II, 2004

Let (Ω,F,P)(\Omega, \mathcal{F}, \mathbb{P}) be a probability space and let A=(Ai:i=1,2,)\mathcal{A}=\left(A_{i}: i=1,2, \ldots\right) be a sequence of events.

(a) What is meant by saying that A\mathcal{A} is a family of independent events?

(b) Define the events {An\left\{A_{n}\right. infinitely often }\} and {An\left\{A_{n}\right. eventually }\}. State and prove the two Borel-Cantelli lemmas for A\mathcal{A}.

(c) Let X1,X2,X_{1}, X_{2}, \ldots be the outcomes of a sequence of independent flips of a fair coin,

P(Xi=0)=P(Xi=1)=12 for i1\mathbb{P}\left(X_{i}=0\right)=\mathbb{P}\left(X_{i}=1\right)=\frac{1}{2} \quad \text { for } i \geqslant 1

Let LnL_{n} be the length of the run beginning at the nth n^{\text {th }}flip. For example, if the first fourteen outcomes are 01110010000110 , then L1=1,L2=3,L3=2L_{1}=1, L_{2}=3, L_{3}=2, etc.

Show that

P(lim supnLnlog2n>1)=0\mathbb{P}\left(\limsup _{n \rightarrow \infty} \frac{L_{n}}{\log _{2} n}>1\right)=0

and furthermore that

P(lim supnLnlog2n=1)=1\mathbb{P}\left(\limsup _{n \rightarrow \infty} \frac{L_{n}}{\log _{2} n}=1\right)=1

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