Prove that the function given with f(x) is a PDF

In summary: Ultimately the only way to sum power series is to relate them to power series of known functions. There are other ways of showing that \sum_{m=0}^\infty \frac{(m + r - 1)!}{(r-1)!m!}q^m = (1 - q)^{-r}. For example, using the formula \frac{d^k}{dx^k} \left( \sum_{n=0}^\infty a_n x^n \right) = \sum_{n=0}^\infty a_{n+k} \frac{(n + k)!}{n!} x^n we see that \sum_{m=0}^\infty
  • #1
MrKushtrim
3
0
Hi,
Could someone help me prove that the function given with
##f(x) = \binom{x-1}{r-1} p^{r}(1-p)^{x-r}## is a probability density function, where ## x= r, r+1,..., \infty ## and ## 0<p<1 ##


I thought to solve it somehow by using the binomial theorem, but since it's the upper part that's changing on the binomial coefficients, this proved futile.
 
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  • #2
Let's start simple:

Can you prove that the value is always larger than zero?
 
  • #3
Use ## instead of $ for inline LaTeX.
 
  • #4
MrKushtrim said:
Hi,
Could someone help me prove that the function given with
[tex]f(x) = \binom{x-1}{r-1} p^{r}(1-p)^{x-r}[/tex] is a probability density function, where [itex]x= r, r+1,..., \infty [/itex] and [itex]0<p<1[/itex]


I thought to solve it somehow by using the binomial theorem, but since it's the upper part that's changing on the binomial coefficients, this proved futile.

You need to show that [tex]\sum_{x=r}^\infty \binom{x-1}{r-1} p^r (1-p)^{x-r} = 1.[/tex] Setting [itex]q = 1- p[/itex] and [itex]m = x - r[/itex] gives [tex]
\sum_{x=r}^\infty \binom{x-1}{r-1} p^r (1-p)^{x-r}
= (1 - q)^r \sum_{m=0}^\infty \frac{(m + r - 1)!}{(r-1)!m!}q^m.[/tex] What does the series on the right have to equal if the left hand side is to equal 1, and can you prove that it does?
 
  • #5
MrKushtrim said:
Hi,
Could someone help me prove that the function given with
$f(x) = \binom{x-1}{r-1} p^{r}(1-p)^{x-r}$ is a probability density function, where $x= r, r+1,..., \infty $ and $0<p<1$


I thought to solve it somehow by using the binomial theorem, but since it's the upper part that's changing on the binomial coefficients, this proved futile.

For discrete random variables, I prefer to use symbols like ##j## and ##k## instead of ##x##. So, in this notation: what properties must be possessed by
[tex]f_k = {k-1 \choose r-1} p^r (1-p)^{k-r}, \, k = r, r+1, r+2, \ldots [/tex]
in order that it be a legitimate probability mass function of a discrete random variable? Note that ##f_k## is NOT a probability density function; it is a probability mass function. There is a world of difference!
 
  • #6
Hi, thanks for the reply .
pasmith said:
You need to show that [tex]\sum_{x=r}^\infty \binom{x-1}{r-1} p^r (1-p)^{x-r} = 1.[/tex] Setting [itex]q = 1- p[/itex] and [itex]m = x - r[/itex] gives [tex]
\sum_{x=r}^\infty \binom{x-1}{r-1} p^r (1-p)^{x-r}
= (1 - q)^r \sum_{m=0}^\infty \frac{(m + r - 1)!}{(r-1)!m!}q^m.[/tex] What does the series on the right have to equal if the left hand side is to equal 1, and can you prove that it does?
It must equal 1. Though I have no idea how to prove it.
Ray Vickson said:
For discrete random variables, I prefer to use symbols like ##j## and ##k## instead of ##x##. So, in this notation: what properties must be possessed by
[tex]f_k = {k-1 \choose r-1} p^r (1-p)^{k-r}, \, k = r, r+1, r+2, \ldots [/tex]
in order that it be a legitimate probability mass function of a discrete random variable? Note that ##f_k## is NOT a probability density function; it is a probability mass function. There is a world of difference!
Thanks, I had the terminology mixed up.
To be a probability mass function , ##f_k## has to be positive, and the sum over all ## k ## has to be 1.
It's easy to see that ##f_k## is positive, but I cannot seem to prove the second property
 
  • #7
MrKushtrim said:
It must equal 1. Though I have no idea how to prove it.

Remember, you need to show that
[tex]
(1 -q)^r \sum_{m=0}^\infty \frac{(m + r - 1)!}{(r-1)!m!}q^m = 1
[/tex] for every [itex]0 < q < 1[/itex]. This suggests that the series [tex]\sum_{m=0}^\infty \frac{(m + r - 1)!}{(r-1)!m!}q^m[/tex] must be the binomial expansion of ... what function of [itex]q[/itex]?
 
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  • #8
pasmith said:
Remember, you need to show that
[tex]
(1 -q)^r \sum_{m=0}^\infty \frac{(m + r - 1)!}{(r-1)!m!}q^m = 1
[/tex] for every [itex]0 < q < 1[/itex]. This suggests that the series [tex]\sum_{m=0}^\infty \frac{(m + r - 1)!}{(r-1)!m!}q^m[/tex] must be the binomial expansion of ... what function of [itex]q[/itex]?

## \frac{1}{(1 -q)^r } ##
But how would I prove that ?
---
Edit:
I checked a wikipedia article about binomial series, and it turns out that the series [tex]\sum_{m=0}^\infty \frac{(m + r - 1)!}{(r-1)!m!}q^m[/tex] is of this form , where ##β= r-1## and ##z=q##

Thanks for the help :)
Though, is there any other way to prove it, without directly using the formula ?
 
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  • #9
MrKushtrim said:
## \frac{1}{(1 -q)^r } ##
But how would I prove that ?
---
Edit:
I checked a wikipedia article about binomial series, and it turns out that the series [tex]\sum_{m=0}^\infty \frac{(m + r - 1)!}{(r-1)!m!}q^m[/tex] is of this form , where ##β= r-1## and ##z=q##

Thanks for the help :)
Though, is there any other way to prove it, without directly using the formula ?

Ultimately the only way to sum power series is to relate them to power series of known functions.

There are other ways of showing that [itex]\sum_{m=0}^\infty \frac{(m + r - 1)!}{(r-1)!m!}q^m = (1 - q)^{-r}[/itex]. For example, using the formula [tex]
\frac{d^k}{dx^k} \left( \sum_{n=0}^\infty a_n x^n \right)
= \sum_{n=0}^\infty a_{n+k} \frac{(n + k)!}{n!} x^n[/tex] we see that [tex]
\sum_{m=0}^\infty \frac{(m + r - 1)!}{(r-1)!m!}q^m = \frac{1}{(r - 1)!} \frac{d^{r-1}}{dq^{r-1}} \left( \sum_{m=0}^\infty q^m \right).[/tex] [itex]\sum_{m=0}^\infty q^m[/itex] is a geometric series and is easily summed.
 

FAQ: Prove that the function given with f(x) is a PDF

1. What is a PDF?

A PDF, or probability density function, is a mathematical function that represents the probability distribution of a continuous random variable. It is used to describe the likelihood of a random variable taking on a specific value or range of values.

2. How do you know if a function is a PDF?

A function must meet two criteria to be considered a PDF: it must always be positive, and the total area under the curve must equal 1. This ensures that the function accurately represents the probability distribution of the random variable.

3. Can a function have multiple PDFs?

No, a function can only have one PDF. However, there can be different PDFs for different random variables within the same function.

4. How can you prove that a function is a PDF?

To prove that a function is a PDF, you must show that it meets the two criteria mentioned earlier: it is always positive and the total area under the curve is 1. This can be done through calculus and integration techniques.

5. What is the importance of PDFs in science?

PDFs are essential in many areas of science, including statistics, physics, and engineering. They allow us to understand the likelihood of different outcomes and make predictions based on probability. PDFs also help us to model and analyze complex systems and data.

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