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FeDeX_LaTeX

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## Homework Statement

Let X be a random variable with a Laplace distribution, so that its probability density function

is given by

[tex]f(x) = \frac{1}{2}e^{-|x|}[/tex]

Sketch f(x). Show that its moment generating function M

_{X}(θ) is given by

[tex]M_{X}(\theta) = \frac{1}{1 - \theta^2}[/tex]

and hence find the variance of X.

A frog is jumping up and down, attempting to land on the same spot each time. In fact, in

each of n successive jumps he always lands on a fixed straight line but when he lands from the ith jump (i = 1 , 2 , . . . , n) his displacement from the point from which he jumped is X

_{i}cm, where X

_{i}has the Laplace distribution described above. His displacement from his starting point after n jumps is

Y cm, so that [itex]Y = \sum_{i=1}^{n} X_{i}[/itex].

Each jump is independent of the others.

Obtain the moment generating function for [itex]\frac{Y}{\sqrt{2n}}[/itex] and, by considering its logarithm, show that this moment generating function tends to [itex]e^{\frac{1}{2}x^{2}}[/itex] as n → ∞.

Given that [itex]e^{\frac{1}{2}x^{2}}[/itex] is the moment generating function of the standard Normal random variable, estimate the least number of jumps such that there is a 5% chance that the frog lands 25 cm or more from his starting point.

## Homework Equations

[itex]M_{X}(t) = E(e^{tX}) = \int_{-\infty}^{\infty} e^{tx}f(x)dx[/itex]

## The Attempt at a Solution

I've sketched f(x), which looks like the graph of [itex]\frac{1}{2}e^x[/itex] for x < 0 and [itex]\frac{1}{2}e^{-x}[/itex] for x > 0.

I've found the moment generating function, and deduced that it has mean 0 and variance 2.

However, I'm unable to obtain the moment generating function for [itex]\frac{Y}{\sqrt{2n}}[/itex]. The mark scheme says this:

If [itex]T = \frac{Y}{\sqrt{2n}}[/itex], then [itex]M_{T}(\theta) = E(e^{T\theta}) = E(e^{\theta \sum \frac{X_{i}}{\sqrt{2n}}}) = \prod_{i=1}^{n}E(e^{\frac{\theta}{\sqrt{2n}}X_{i}}) = \left( 1 - \frac{\theta^{2}}{2n} \right)^n[/itex]

I understand everything up until where the last part; how are they turning that product into that neat (1 - theta^2 / 2n)^n term? My approach was to say that all the X

_{i}have the same distribution, so every term in the product is the same, and you get this:

[itex]\left( \frac{1}{\sqrt{2n}}M_{X}(\theta) \right)^n = \left(\frac{1}{\sqrt{2n}(1 - \theta^{2})} \right)^n[/itex]

but this is clearly not equivalent to their answer. What have I done wrong here and what have they done to collapse their product into something so simple?