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Measure, Integral and Probability

Marek Capi?ski, Ekkehard Kopp

Chapter 8

Limit theorems - all with Video Answers

Educators


Chapter Questions

03:55

Problem 1

For each of the following decide whether $f_{n} \rightarrow 0$ (i) in $L^{p}$, (ii) uniformly, (iii) pointwise, (iv) a.e.
(a) $f_{n}=\mathbf{1}_{\left[n, n+\frac{1}{n}\right]}$,
(b) $f_{n}=n \mathbf{1}_{\left[0, \frac{1}{n}\right]}-n \mathbf{1}_{\left[-\frac{1}{n}, 0\right]}$

Nick Johnson
Nick Johnson
Numerade Educator
02:04

Problem 2

Go back to the proof of Theorem $8.1$ (with $E=[0,1]$ ) to see which of the sequences of random variables constructed there converge in probability.

Amany Waheeb
Amany Waheeb
Numerade Educator
01:56

Problem 3

Find an example of a sequence of random variables on $[0,1]$ that does not converge to 0 in probability.

Amany Waheeb
Amany Waheeb
Numerade Educator
02:59

Problem 4

Using Chebyshev's inequality find a lower bound for the probability that the average number of heads in 100 tosses of a coin differs from $\frac{1}{2}$ by 0.1.

Bryan Lynn
Bryan Lynn
Numerade Educator
04:38

Problem 5

Find a lower bound for the probability that the average number shown on a die in 1000 tosses differs from $3.5$ by $0.01$.

Lucas Finney
Lucas Finney
Numerade Educator
01:13

Problem 6

Find $\lim \sup _{n \rightarrow \infty} A_{n}$ for a sequence $A_{1}=[0,1], A_{2}=\left[0, \frac{1}{2}\right], A_{3}=\left[\frac{1}{2}, 1\right]$, $A_{4}=\left[0, \frac{1}{4}\right], A_{5}=\left[\frac{1}{4}, \frac{1}{2}\right]$ etc.

Wendi Zhao
Wendi Zhao
Numerade Educator
02:33

Problem 7

Let $S_{n}=X_{1}+X_{2}+\ldots+X_{n}$ describe the position after $n$ steps of a symmetric random walk on $\mathbb{Z}^{d}$. Using the asymptotic formula: $n ! \sim$ $\left(\frac{n}{c}\right)^{n} \sqrt{2 \pi n}$ and the Borel-Cantelli lemmas show that the probability of $\left\{S_{n}=0\right.$ i.o. $\}$ is 1 when $d=1,2$ and 0 for $d>2$.
We have the following simple but fundamental fact.

Nick Johnson
Nick Johnson
Numerade Educator
02:49

Problem 8

Use the Central Limit Theorem to estimate the probability that the number of Heads in 1000 independent tosses differs from 500 by less than $2 \%$

Jerrah Biggerstaff
Jerrah Biggerstaff
Numerade Educator
00:31

Problem 9

How many tosses of a coin are required to have the probability at least $0.99$ that the average number of Heads differs from $0.5$ by less than $1 \% ?$

Christopher Stanley
Christopher Stanley
Numerade Educator
04:41

Problem 10

Show that
$$
a_{n} \rightarrow\left(r-\frac{1}{2} \sigma^{2}\right) T
$$
For each $n$ we have a sequence of $n$ independent identically distributed random variables $\xi_{n}(i)=\ln \eta_{n}(i)$ forming the so-called triangular array. We have the following version of Central Limit Theorem. It can be proved in exactly the same way as Theorem $8.30$ (see also [2]).

Mengchun Cai
Mengchun Cai
Numerade Educator