Prob of red ball in buckets. Binomial?

In summary, the probability that a particular bucket will get $n$ red balls is:P(X=n)={R \choose n}\left(\frac{1}{M}\right)^n\left(\frac{M-1}{M}\right)^{R-n}=1
  • #1
paolopiace
10
0
I know for many of you this sounds trivial. For me it's not...

A barrel contains M buckets each containing B balls, for a total of N = M x B balls.

N is a big number.

A small number R > 1 of balls are red. All the others N-R are white.

The location of each one of these R balls is random across the M buckets.
That means, one or more red balls can be in any bucket.

Now I randomly pull one bucket from the barrel.

What is the probability that X > 1 red balls are in that bucket?

Besides the final formula that I really need, may I also ask for a guidance to get to it?

Thank You!
 
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  • #2
I think we can ignore the white balls, and just imagine we have $M$ buckets before us, and $R$ red balls to randomly distribute among the buckets. Now, each time we put a red ball into a bucket, what is the probability that for any particular bucket, it will be the one that gets the ball?
 
  • #3
MarkFL said:
... what is the probability that for any particular bucket, it will be the one that gets the ball?

I think there are R/M probabilities to see one red ball in a particular bucket.
Then, there are (R-1)/M probabilities to see a second red ball in that same bucket.
Therefore R(R-1)/M^2 is the prob. to find two red balls in a particular bucket.

Kind of right? Or not?
 
  • #4
Each time we place a red ball into a bucket, the probability that a particular bucket will get the ball is:

\(\displaystyle P(X)=\frac{1}{M}\)

What is the probability that the bucket will not get the ball?
 
  • #5
MarkFL said:
... What is the probability that the bucket will not get the ball?

It's 1-1/M

I am kind of confused. If a throw R balls in the air, there are R/M probabilities that a specific bucket gets a ball. Or not?

1/M is the probability that a specific bucket gets a ball if I throw one and only one ball. Or nor?
 
Last edited:
  • #6
paolopiace said:
It's 1-1/M

I am kind of confused. If a throw R balls in the air, there are R/M probabilities that a specific bucket gets a ball. Or not?

Yes, that's right about a bucket not getting a ball. I am looking at this from the perspective that each placement of a red ball is a separate event. I find it simpler that way. :)

So using what we have found so far, what is the probability that a certain bucket will get $n$ red balls, where $0\le n\le R$?
 
  • #7
MarkFL said:
Yes, that's right about a bucket not getting a ball. I am looking at this from the perspective that each placement of a red ball is a separate event. I find it simpler that way. :)

So using what we have found so far, what is the probability that a certain bucket will get $n$ red balls, where $0\le n\le R$?

Let's look at a simple example...we have 3 red balls ($R=3$). Now suppose we want to compute the probability that a particular bucket gets 1 red ball. So, using what we have found, we would have:

\(\displaystyle \left(\frac{1}{M}\right)^1\left(\frac{M-1}{M}\right)^2\)

But...this is only the probability for one of the ways the bucket could get 1 red ball...it could get the red ball on the first distribution, or on the second, or on the third. So, the probability that a bucket will get 1 red ball is:

\(\displaystyle P(X=1)=3\left(\frac{1}{M}\right)^1\left(\frac{M-1}{M}\right)^2\)

Now, suppose we generalize back to $R$ red balls and $n$ red balls in a particular bucket. There will be $R$ distributions, and so we have $R$ "slots" to fill with either "Yes, got a ball" or "No, did not get a ball." Thus, we have $n$ yeses to distribute into $R$ slots.

For the first yes, we have $R$ choices, for the second we have $R-1$ choices and continuing all the way down to the $n$th yes, where we have $R-(n-1)$ choices. But, we must account for the fact that a yes going into a particular slot is equivalent to a yes going into that same slot on another choice. So, we must divide by the number of ways to arrange $n$ items, which is $n!$.

Putting all of this together, we find the number $N$ of distinct ways to distribute these yeses into the $R$ slots is given by:

\(\displaystyle N=\frac{R(R-1)(R-2)\cdots(R-(n-1))}{n!}=\frac{R!}{n!(R-n)!}={R \choose n}\)

What we have in fact just found is the number of ways to choose $n$ from $R$. :)

And so we find the probability that a particular bucket gets $n$ red balls is:

\(\displaystyle P(X=n)={R \choose n}\left(\frac{1}{M}\right)^n\left(\frac{M-1}{M}\right)^{R-n}\)

Now, if this is correct, then we should find that the sum of the probabilities for $n=0$ to $n=R$ is 1, since it is certain that any bucket will get some number of red balls from $0$ to $R$:

\(\displaystyle \sum_{n=0}^R\left({R \choose n}\left(\frac{1}{M}\right)^n\left(\frac{M-1}{M}\right)^{R-n}\right)=1\)

We should recognize that the LHS of the above equation is an application of the binomial theorem, and so we may write:

\(\displaystyle \left(\frac{1}{M}+\frac{M-1}{M}\right)^R=1\)

\(\displaystyle \left(\frac{M-1+1}{M}\right)^R=1\)

\(\displaystyle \left(\frac{M}{M}\right)^R=1\)

\(\displaystyle (1)^R=1\)

\(\displaystyle 1=1\)

So, our formula checks out and we have in essence derived the binomial probability formula. Can you now use this formula to answer the given question:

\(\displaystyle P(X>1)=?\)
 
  • #8
Just to finish this up, we have:

\(\displaystyle \sum_{n=0}^R\left({R \choose n}\left(\frac{1}{M}\right)^n\left(\frac{M-1}{M}\right)^{R-n}\right)=1\)

And we can write this as:

\(\displaystyle {R \choose 0}\left(\frac{1}{M}\right)^0\left(\frac{M-1}{M}\right)^{R-0}+{R \choose 1}\left(\frac{1}{M}\right)^1\left(\frac{M-1}{M}\right)^{R-1}+\sum_{n=2}^R\left({R \choose n}\left(\frac{1}{M}\right)^n\left(\frac{M-1}{M}\right)^{R-n}\right)=1\)

Now, we should recognize that:

\(\displaystyle P(X>1)=\sum_{n=2}^R\left({R \choose n}\left(\frac{1}{M}\right)^n\left(\frac{M-1}{M}\right)^{R-n}\right)\)

And so, making that substitution, and simplifying, we obtain:

\(\displaystyle \left(\frac{M-1}{M}\right)^{R}+R\left(\frac{1}{M}\right)\left(\frac{M-1}{M}\right)^{R-1}+P(X>1)=1\)

Solving for $P(X>1)$, we find:

\(\displaystyle P(X>1)=1-\left(\frac{M-1}{M}\right)^{R}-\frac{R}{M}\left(\frac{M-1}{M}\right)^{R-1}\)
 
  • #9
MarkFL said:
Just to finish this up, we have: ...

This is beautiful!
I want to thank you so much, although I still have to digest it entirely.
Sorry for this late feedback. Many things of life kept me away from my post.
 

FAQ: Prob of red ball in buckets. Binomial?

What is the binomial distribution?

The binomial distribution is a probability distribution that describes the likelihood of obtaining a certain number of successes in a fixed number of independent trials with two possible outcomes, such as success or failure.

How is the binomial distribution related to the probability of a red ball in buckets?

The binomial distribution can be used to calculate the probability of obtaining a certain number of red balls in a fixed number of trials, assuming the probability of success (i.e. getting a red ball) remains constant for each trial.

What is the formula for calculating the probability of a red ball in buckets using the binomial distribution?

The formula is P(x) = nCx * p^x * (1-p)^(n-x), where n is the number of trials, x is the number of successes, and p is the probability of success.

What is the difference between the binomial distribution and the probability of a red ball in buckets?

The binomial distribution is a mathematical model used to calculate the probability of a certain number of successes in a fixed number of trials, while the probability of a red ball in buckets refers to the actual likelihood of obtaining a red ball in a given set of buckets.

How can the binomial distribution be applied in real-life situations?

The binomial distribution can be applied in various fields, such as genetics, finance, and manufacturing, to predict the likelihood of a certain number of successes or failures in a specific number of trials. For example, it can be used to determine the probability of a certain number of defective products in a batch of manufactured goods.

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