Binomial Distribution Probability

In summary, the conversation discusses a problem involving a Binomial B(\frac{1}{2}, n) where n=2m. The goal is to show that the limit of a function a(m,k) = \frac{4^m}{(\stackrel{2m}{m})}P(X = m + k)^2 approaches e^{-k^2} as m approaches infinity. The participants have derived the expression for a(m,k) and simplified it for k=3, showing that the limit is 1. However, there is confusion about whether this result holds for all values of k and if the problem is stated correctly. Further clarification or discussion with a teacher may be needed.
  • #1
azdang
84
0
Let X be a Binomial B([tex]\frac{1}{2}[/tex],n), where n=2m.

Let a(m,k) = [tex]\frac{4^m}{(\stackrel{2m}{m})}P(X = m + k)[/tex].

Show that [tex]lim_{m->\infty}(a(m,k))^2 = e^{-k^2}[/tex].

So far, I've found that P(X = m+k) = [tex](\stackrel{2m}{m+k}) \frac{1}{4^m}[/tex]

Then, a(m,k)=[tex]\frac{m!m!}{(m+k)!(m-k)!}.[/tex]

But I have no idea how to show that the limit of [tex]a^2[/tex] will be equal to [tex]e^{-k^2}[/tex].

I think my work up to that point seems okay. I got things to cancel out, so that is usually a good sign. Any hints? Thank you!
 
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  • #2
I got what you got, and this is not a good sign. If we are correct, then look at the k=3 case.

a(m,3) simplifies to [tex]\frac{m(m-1)(m-2)}{(m+3)(m+2)(m+1)}[/tex] and the limit is 1 as m approaches infinity.
 
  • #3
Well, we are looking for the limit of [tex]a^2[/tex]. Does that make a difference?

Also, you said you got the same thing as me, but I'm a little confused how what you wrote (even though I know it's for the specific k=3 case) is the same as what I got. I'll look at it again to see if I can see the connection, but maybe what I got for a was wrong?
 
  • #4
[tex]a(m,3)=\frac{m!m!}{(m+3)!(m-3)!}=\frac{m!\cdot m(m-1)(m-2)(m-3)!}{(m+3)(m+2)(m+1)m!\cdot (m-3)!}
=\frac{m(m-1)(m-2)}{(m+3)(m+2)(m+1)}[/tex]

and

[tex]\lim_{m\to\infty}a(m,3)^2=\left(\lim_{m\to\infty}a(m,3)\right)^2=1^2=1[/tex]



I think you should check if the problem is stated correctly. I get the same thing as you did for a(m,k) every time I check.
 
  • #5
Yep, that's definitely what the problem says so, I'm not sure.

What you've shown definitely seems right and I'm assuming it works for other cases when k is something else, as well. I'll check it out and ask my teacher about it if need be. Thanks!
 

What is Binomial Distribution Probability?

Binomial Distribution Probability is a statistical concept that describes the probability of a certain number of successes in a fixed number of independent trials, given a specific probability of success.

How is Binomial Distribution Probability calculated?

The formula for calculating Binomial Distribution Probability is: P(x) = (nCx) * px * (1-p)n-x, where n is the number of trials, x is the number of successes, and p is the probability of success in each trial. The symbol (nCx) represents the combination of n things taken x at a time.

What types of experiments follow a Binomial Distribution Probability?

Experiments that have a fixed number of trials, a dichotomous outcome (success or failure), and a constant probability of success in each trial follow a Binomial Distribution Probability. Examples include coin tosses, dice rolls, and medical trials.

How is Binomial Distribution Probability different from Normal Distribution Probability?

Binomial Distribution Probability describes the probability of a discrete number of successes in a fixed number of trials, while Normal Distribution Probability describes the probability of a continuous range of outcomes. Additionally, Binomial Distribution Probability assumes a fixed probability of success in each trial, while Normal Distribution Probability assumes a continuous probability distribution.

What are the key properties of a Binomial Distribution Probability?

The key properties of a Binomial Distribution Probability include a fixed number of trials, a constant probability of success in each trial, a dichotomous outcome, and the independence of each trial from the others. It also follows a bell-shaped curve when the number of trials is large.

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