Sterling approximation of Beta Function

In summary, Sterling's approximation is used to approximate the limit of n^x \frac{\Gamma(n)}{\Gamma(n+x)} and the simplified Stirling formula is used to get the result.
  • #1
dykuma
56
7

Homework Statement


upload_2016-11-1_15-37-21.png

Homework Equations


upload_2016-11-1_15-37-31.png

upload_2016-11-1_15-37-41.png

The Attempt at a Solution


I think this problem is probably a lot simpler than I am making it out to be. However, when I apply sterling's approx., I get a very nasty equation that does not simplify easily.
upload_2016-11-1_15-39-48.png


One of the biggest problems I have though is dealing with that n^x term. it seems to me that as n approaches infinity, the outcome depends heavily on whether x is a positive or negative integer, or if its zero.
 
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  • #2
Rearrange it to:

[itex]n^x B(x,n) =\Gamma(x) n^x \frac{\Gamma(n)}{\Gamma(n+x)}[/itex]

So what you're really interested in is the limit of

[itex]n^x \frac{\Gamma(n)}{\Gamma(n+x)}[/itex]

Use Sterling's approximation:

[itex]\Gamma(n) \approx n^{n+\frac{1}{2}} \sqrt{2 \pi} e^{-n}[/itex]
[itex]\Gamma(n+x) \approx (n+x)^{n+x+\frac{1}{2}} \sqrt{2 \pi} e^{-(n+x)}[/itex]

Write [itex](n+x)^{n+x+\frac{1}{2}}[/itex] as [itex](n(1+\frac{x}{n}))^{n+x +\frac{1}{2}} = n^{n+x+\frac{1}{2}} (1+\frac{x}{n})^{n+x+\frac{1}{2}}[/itex]
[itex]= n^{n+\frac{1}{2}} n^x (1+\frac{x}{n})^{n} (1+\frac{x}{n})^{x+\frac{1}{2}}[/itex]

So
[itex]\Gamma(n+x) \approx n^{n+\frac{1}{2}} n^x (1+\frac{x}{n})^{n} (1+\frac{x}{n})^{x+\frac{1}{2}} \sqrt{2 \pi} e^{-(n+x)}[/itex]

Now, you can just use a fact about [itex]e[/itex]: [itex]e^x = lim_{n \rightarrow \infty} (1+\frac{x}{n})^n[/itex]
 
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  • #3
Suggest using the simplified Stirling formula without the ## \sqrt{2 \pi n} ##.Then the result follows immediately with ## ln(n+x-1)=ln(n) ## in the limit that n approaches infinity... editing...You can include the ## \sqrt{2 \pi n} ## but in the limit that n approaches infinity, this puts the same factor in the numerator that it does in the denominator.
 
Last edited:
  • #4
Thanks to both of you! The key to solving that problem noticing the what Steven showed, that
stevendaryl said:
ex=limn→∞(1+xn)n

Then the entire problem simplifies quite nicely.
 

1. What is the Sterling approximation of Beta Function?

The Sterling approximation of Beta Function is a mathematical formula that approximates the value of the Beta Function, which is a special function in mathematics that is used to calculate the area under the curve of a Beta distribution. It is named after the British mathematician James Sterling, who first derived the formula.

2. How is the Sterling approximation of Beta Function calculated?

The Sterling approximation of Beta Function is calculated using the Gamma function, which is a generalization of the factorial function. The formula is:

B(x,y) ≈ √(2π)/y * (x/e)^x * (y/e)^y * e^(1/12x-1/12y+1/(360x^3)-1/(1260y^3))

where x and y are the two parameters of the Beta Function.

3. What are the applications of the Sterling approximation of Beta Function?

The Sterling approximation of Beta Function is used in various fields of science and engineering, including statistics, physics, and economics. It can be used to approximate the probability of an event occurring, estimate the reliability of a system, and calculate the average waiting time in queueing theory.

4. Is the Sterling approximation of Beta Function accurate?

The accuracy of the Sterling approximation of Beta Function depends on the values of the parameters x and y. For large values of x and y, the approximation is very accurate, but for small values, it may deviate significantly from the actual value. However, it is still a useful tool for quick estimations and calculations.

5. Are there any limitations to the use of the Sterling approximation of Beta Function?

Yes, the Sterling approximation of Beta Function is limited to only certain values of the parameters x and y. It is not valid for negative or complex values, and it may also produce inaccurate results for small values. In these cases, it is better to use the actual Beta Function formula instead of the approximation.

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