Moments from characteristic function geometric distribution

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SUMMARY

The discussion focuses on finding the characteristic function of a geometric distribution defined by the probability density function \( p_n = (1-p)^n p \) for \( n = 0, 1, 2, \ldots \). The user attempts to derive the characteristic function \( p(k) = \langle e^{ikn} \rangle \) and subsequently determine the mean and variance. The confusion arises from incorrectly equating the series \( \sum (1-p)^n p e^{-ikn} \) with \( \sum (1-p)^n p \frac{(-ik)^n}{n!} \), leading to a misunderstanding of the relationship between the terms in the series.

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binbagsss
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Homework Statement



Hi,

I have the probability density: ##p_{n}=(1-p)^{n}p , n=0,1,2... ##

and I am asked to find the characteristic function: ##p(k)= <e^{ikn}> ## and then use this to determine the mean and variance of the distribution.

Homework Equations


[/B]
I have the general expression for the characteristic function : ##\sum\limits^{\infty}_{n=0} \frac{(-ik)^m}{m!} <x^{m}> ## * , from which can equate coefficients of ##k## to find the moments.

The Attempt at a Solution



So I have ## <e^{-ikn}>=\sum\limits^{\infty}_{n-0} (1-p)^{n}p e^{-ikn} ##

I understand the solution given in my notes which is that this is equal to, after some rearranging etc, expanding out using taylor :

## 1 + \frac{(1-p)}{ p} (-k + 1/2 (-ik)^{2} + O(k^3) ) + \frac{(1-p)^{2}} { p^2 } ( (-ik)^{2} + O(k^3))##
and then equating coefficients according to *

However my method was to do the following , and I'm unsure why it is wrong:

## <e^{-ikn}>=\sum\limits^{\infty}_{n=0} (1-p)^{n} p e^{-ikn} = \sum\limits^{\infty}_{n=0} (1-p)^{n}p \frac{(-ik)^n}{n!} ##

And so comparing to * ## \implies ##

## \sum\limits^{\infty}_{n=0} (1-p)^{n}p = \sum\limits^{\infty}_{n=0} <x^{n}> ##

Anyone tell me what I've done wrong? thank you, greatly appreciated.
 
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binbagsss said:

Homework Statement



Hi,

I have the probability density: ##p_{n}=(1-p)^{n}p , n=0,1,2... ##

and I am asked to find the characteristic function: ##p(k)= <e^{ikn}> ## and then use this to determine the mean and variance of the distribution.

Homework Equations


[/B]
I have the general expression for the characteristic function : ##\sum\limits^{\infty}_{n=0} \frac{(-ik)^m}{m!} <x^{m}> ## * , from which can equate coefficients of ##k## to find the moments.

The Attempt at a Solution



So I have ## <e^{-ikn}>=\sum\limits^{\infty}_{n-0} (1-p)^{n}p e^{-ikn} ##

I understand the solution given in my notes which is that this is equal to, after some rearranging etc, expanding out using taylor :

## 1 + \frac{(1-p)}{ p} (-k + 1/2 (-ik)^{2} + O(k^3) ) + \frac{(1-p)^{2}} { p^2 } ( (-ik)^{2} + O(k^3))##
and then equating coefficients according to *

However my method was to do the following , and I'm unsure why it is wrong:

## <e^{-ikn}>=\sum\limits^{\infty}_{n=0} (1-p)^{n} p e^{-ikn} = \sum\limits^{\infty}_{n=0} (1-p)^{n}p \frac{(-ik)^n}{n!} ##

And so comparing to * ## \implies ##

## \sum\limits^{\infty}_{n=0} (1-p)^{n}p = \sum\limits^{\infty}_{n=0} <x^{n}> ##

Anyone tell me what I've done wrong? thank you, greatly appreciated.

Simplify:
$$p (1-p)^n e^{-ikn} = p x^n, \; \text{where} \; x = (1-p)e^{-ik}$$
Now just sum the geometric series ##\sum x^n##.
 
Ray Vickson said:
Simplify:
$$p (1-p)^n e^{-ikn} = p x^n, \; \text{where} \; x = (1-p)e^{-ik}$$
Now just sum the geometric series ##\sum x^n##.

Yeah that's fine, and in the end you get the 'solution given' which I said I understand. That's what's been done before expanding out.

My problem was wondering what is wrong with the method I post after that...
 
Last edited:
binbagsss said:
Yeah that's fine, and in the end you get the 'solution given' which I said I understand. That's what's been done before expanding out.

My problem was wondering what is wrong with the method I post after that...

You seem to be saying that ##p(1-p)^n e^{-ikn}## is the same as ##p(1-p)^n (-ik)^n/n!##, but this is obviously false.
 
Last edited:
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