How Is the Permutation Approximation Proven for Large N?

In summary, permutation approximation is a statistical method used in scientific research to estimate the probability of a certain outcome by rearranging a set of numbers or objects in different ways. It is particularly useful in situations where traditional statistical tests may not be appropriate and does not rely on strict assumptions about the underlying distribution of the data. However, it may not be suitable for heavily skewed or small sample size data and requires a large number of permutations. Permutation approximation can be applied to a variety of data types, but it is important to ensure that the necessary assumptions and conditions are met.
  • #1
ppedro
22
0
How can I prove that, for [itex]N\gg n[/itex]

[itex]\frac{N!}{(N-n)!}\approx N^{n}[/itex]

I've tried doing

[itex]\frac{N!}{(N-n)!}=\exp\left(\ln\frac{N!}{(N-n)!}\right)=\exp\left(\ln N!-\ln\left(N-n\right)!\right)[/itex]

[itex]\underset{stirling}{\approx}\exp\left(N\ln N-N-\left(N-n\right)\ln\left(N-n\right)+N-n\right)[/itex]

[itex]=N^{N}+\left(N-n\right)^{n-N}+\exp-n[/itex]

But it doesn't look like I'm getting there
 
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  • #2
I don't think you need Stirling's Approximation to show this:

[tex]\frac{N!}{(N-n)!} = N(N-1)...(N-n+1)[/tex]
[tex]\frac{N!}{N^n(N-n)!} = \left(1-\frac{1}{N}\right)\left(1-\frac{2}{N}\right)...\left(1-\frac{n-1}{N}\right)[/tex]

So for any fixed [itex]n[/itex], [itex]N^n[/itex] becomes a better approximation as [itex]N[/itex] becomes larger. (How good depending on the size of [itex]n[/itex]).
 
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  • #3
I understand what you wrote in the first line. I have n factors involving N minus something which is to be small compared with N. But I should be able to say it formally.
 
  • #4
Well to avoid the ...'s, you can use the [itex]\prod[/itex] product notation, and then take the limit as N goes to infinity of both sides, using the properties of the limit of a finite product.
 
  • #5
I'm afraid I don't know how to do it. Could you write it explicitly? Many thanks
 
  • #6
You were almost there in the first post -- you just need to use the fact that n is much smaller than N.

How big is n? Let's say that ##n = \log N##. (If n is too big, it won't be true any more!)

Then ##\log(N - n) = \log\left(N - \log N\right) = \log N - \int_{N-\log N}^N {1 \over x}\,dx.## And ##\int_{N-\log N}^N {1 \over x}\,dx = {\log N \over N} + O\left((\log N)^2 \over N^2\right),## so
##\log(N - n) = \log N - {\log N \over N} + O\left((\log N)^2 \over N^2\right)##.

Now you can do the last step:

##N\log N - (N−n)\log(N−n) − n = N\log N - (N-\log N)\left(\log N - {\log N \over N} + O\left((\log N)^2 \over N^2\right)\right) - \log N = (\log N)^2 + O\left((\log N)^2/N\right).## Then you can exponentiate to get the answer you wanted (don't forget that ##n = \log N##).

I hope I did all the math correctly there...
 
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  • #7
I saw I made a careless mistake on the last step of my approach. I was summing the terms where they should be multiplied. So I end up with

[itex]=N^{N}\left(N-n\right)^{n-N}e^{-n}=e^{-n}\frac{N^{N}}{\left(N-n\right)^{N-n}}=e^{-n}N^{n}\left(\frac{N}{N-n}\right)^{N-n}[/itex]

and I see that[itex] \left(\frac{N}{N-n}\right)^{N-n}=e^{n}[/itex] leading to the final answer.
 
  • #8
ppedro said:
I'm afraid I don't know how to do it. Could you write it explicitly? Many thanks

Oh sorry I should've been more clear I guess:

[tex]\frac{N!}{(N-n)!} = \prod_{k=0}^{n-1}(N-k)[/tex]
[tex]\frac{N!}{N^n(N-n)!} = \frac{1}{N^n}\prod_{k=0}^{n-1}(N-k)[/tex]
[tex]\frac{N!}{N^n(N-n)!} = \left(\prod_{k=0}^{n-1}\frac{1}{N}\right)\left(\prod_{k=0}^{n-1}(N-k)\right)[/tex]
[tex]\frac{N!}{N^n(N-n)!} = \prod_{k=0}^{n-1}\frac{N-k}{N}[/tex]
[tex]\frac{N!}{N^n(N-n)!} = \prod_{k=0}^{n-1}\left(1-\frac{k}{N}\right)[/tex]

Now, assuming [itex]n[/itex] is constant, take the limit as [itex]N[/itex] approaches [itex]∞[/itex].
[tex] \lim_{N \rightarrow ∞} \frac{N!}{N^n(N-n)!} = \lim_{N \rightarrow ∞}\prod_{k=0}^{n-1}\left(1-\frac{k}{N}\right) = \prod_{k=0}^{n-1}\lim_{N \rightarrow ∞}\left(1-\frac{k}{N}\right) = \prod_{k=0}^{n-1}1 = 1[/tex]

which proves that for large enough [itex]N[/itex], the ratio of [itex]\frac{N!}{N^n(N-n)!} ≈ 1 [/itex]. So the two expressions are asymptotically the same (in terms of ratio).
 

Related to How Is the Permutation Approximation Proven for Large N?

1. What is permutation approximation?

Permutation approximation is a statistical method used to estimate the probability of a certain outcome by rearranging a set of numbers or objects in different ways.

2. How is permutation approximation used in scientific research?

Permutation approximation is commonly used in scientific research to test hypotheses and analyze data. It is particularly useful in situations where traditional statistical tests may not be appropriate, such as with small sample sizes or non-normal distributions.

3. What is the difference between permutation approximation and traditional statistical tests?

The main difference between permutation approximation and traditional statistical tests is that permutation approximation does not rely on strict assumptions about the underlying distribution of the data. Instead, it uses a data-driven approach by randomly rearranging the data to simulate all possible outcomes and estimate the probability of the observed result.

4. What are the limitations of permutation approximation?

Permutation approximation may not be appropriate in certain situations, such as when the data is heavily skewed or has a small sample size. It also requires a large number of permutations to accurately estimate the probability, which may be computationally intensive.

5. Can permutation approximation be used for all types of data?

Permutation approximation can be used for a wide range of data types, including continuous, categorical, and non-parametric data. However, it is important to ensure that the assumptions and conditions for using permutation approximation are met before applying the method to a particular dataset.

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