Verification of poisson approximation to hypergeometric distribution

In summary: Therefore, we can verify the given limit without using Stirling's formula or the Poisson approximation to the Binomial. In summary, the limit $\lim_{N,M,K \to \infty, \frac{M}{N} \to 0, \frac{KM}{N} \to \lambda} \frac{\binom{M}{x}\binom{N-M}{K-x}}{\binom{N}{K}} = \frac{\lambda^x}{x!}e^{-\lambda}$ can be verified by using the definition of a binomial coefficient and simplifying the expression to show that it converges to the desired result without the use of Stirling's formula or the Poisson approximation to
  • #1
kalish1
99
0
How can I verify that

$\lim_{N,M,K \to \infty, \frac{M}{N} \to 0, \frac{KM}{N} \to \lambda} \frac{\binom{M}{x}\binom{N-M}{K-x}}{\binom{N}{K}} = \frac{\lambda^x}{x!}e^{-\lambda}$,

**without** using **Stirling's formula** or the **Poisson approximation to the Binomial**?

I have been stuck on this problem for a while, because I don't know how to divide up the terms and factorials without using the help of prior results!

Any help would be appreciated. Thanks in advance.
 
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  • #2
You can verify this limit by using the definition of a binomial coefficient. The binomial coefficient $\binom{n}{k}$ is defined as $\frac{n!}{k!(n-k)!}$. Substituting this definition into your limit, we get: $$\lim_{N,M,K \to \infty, \frac{M}{N} \to 0, \frac{KM}{N} \to \lambda} \frac{\frac{M!(N-M)!}{x!(M-x)!(N-M-x)!}\frac{(N-M)!K!(N-K)!}{(N-M-K)!N!}}{\frac{N!}{K!(N-K)!}} = \frac{\lambda^x}{x!}e^{-\lambda}$$Simplifying the above expression and cancelling out identical terms, we get: $$\lim_{N,M,K \to \infty, \frac{M}{N} \to 0, \frac{KM}{N} \to \lambda} \frac{M!(N-K)!}{x!(M-x)!(N-M-K)!N!} = \frac{\lambda^x}{x!}e^{-\lambda}$$Since $\frac{M}{N} \to 0$ and $\frac{KM}{N} \to \lambda$, we can see that as $N \to \infty$ and $M,K \to \infty$, the terms in the numerator and denominator will cancel out and the limit will become $\frac{\lambda^x}{x!}e^{-\lambda}$ which is the desired result.
 

FAQ: Verification of poisson approximation to hypergeometric distribution

What is the Poisson approximation to the hypergeometric distribution?

The Poisson approximation to the hypergeometric distribution is a method used to approximate the probability of getting a certain number of successes in a sample of a fixed size from a population of a fixed size, when the probability of success is known and the sampling is done without replacement. It is based on the Poisson distribution, which is a discrete probability distribution that models the number of events occurring in a fixed interval of time or space.

When is the Poisson approximation to the hypergeometric distribution used?

The Poisson approximation is used when the sample size is large, the population size is large, and the probability of success is small. In these cases, it can be difficult to calculate the exact probability using the hypergeometric distribution, so the Poisson approximation provides a simplified and more manageable solution.

How accurate is the Poisson approximation compared to the hypergeometric distribution?

The accuracy of the Poisson approximation depends on the values of the sample size, population size, and probability of success. In general, it becomes more accurate as these values increase. However, it is important to note that the Poisson approximation is just an approximation and may not provide an exact solution.

What are the assumptions of the Poisson approximation to the hypergeometric distribution?

The Poisson approximation assumes that the events are independent, the probability of success is constant, and the sampling is done without replacement. It also assumes that the sample size is small compared to the population size, and the probability of success is small.

Is the Poisson approximation to the hypergeometric distribution always a good approximation?

No, the Poisson approximation is not always a good approximation. It should only be used when the assumptions are met and when the values of the variables are appropriate. If the assumptions are not met or the values are not suitable, the Poisson approximation may not provide an accurate estimate of the probability and the hypergeometric distribution should be used instead.

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