What is the Combinatorial Interpretation of this Sum?

In summary, In this problem, the number of red balls in an urn is determined randomly, and then the probability of selecting a red ball is determined.
  • #1
techmologist
306
12
Is there a combinatorial interpretation of the sum

[tex]\sum_{k=0}^{n}\binom{k}{r}\binom{n-k}{m-r}[/tex] ?

If it were a sum over r instead, it would be n C m. But I don't know about this one.
 
Physics news on Phys.org
  • #2
After plugging in a few numbers and playing around with it, I found that the sum is, surprisingly, equal to (n+1) C (m+1). Maybe knowing this will make it easier to come up with an interpretation.
 
  • #3
Well, there is a sort of literal translation: it's counting the number of ways to:

  • Pick an integer k
  • Choose r elements from {1, ..., k}
  • Choose m-r elements from {1, ..., n-k}

Well, it's easy to reorganize the data:

  • Pick an integer k
  • Choose r elements from {1, ..., k}
  • Choose m-r elements from {k+1, ..., n}

and

  • Choose m elements from {1, ..., n}
  • Pick an integer such that r of the elements are less than or equal to k
 
  • #4
Hurkyl said:
Well, there is a sort of literal translation: it's counting the number of ways to:

  • Pick an integer k
  • Choose r elements from {1, ..., k}
  • Choose m-r elements from {1, ..., n-k}

Well, it's easy to reorganize the data:

  • Pick an integer k
  • Choose r elements from {1, ..., k}
  • Choose m-r elements from {k+1, ..., n}
...

Ah, I see where you are going with that. But isn't that going to come out to n C m?
 
  • #5
Actually, I wasn't going anywhere -- those were just simplifications I saw off the top of my head. I stopped once I noticed a path to start following in earnest!

Do you not see the difference between:
  • A set of m elements in {1, ..., n} along with an integer k such that r of the chosen elements are less than or equal to k,
  • A set of m elements in {1, ..., n}?

Anyways, the path forward I saw was to look for a way to insert an extra object into the setup to represent k.
 
  • #6
Hurkyl said:
Actually, I wasn't going anywhere -- those were just simplifications I saw off the top of my head. I stopped once I noticed a path to start following in earnest!

Do you not see the difference between:
  • A set of m elements in {1, ..., n} along with an integer k such that r of the chosen elements are less than or equal to k,
  • A set of m elements in {1, ..., n}?

Well, yes, but if k is an element of {1,...,n}, then aren't you choosing from n elements? And if it is not in {1, ..., n}, then all m elements are less than k.

Anyways, the path forward I saw was to look for a way to insert an extra object into the setup to represent k.

Okay. Maybe I misunderstood what you were saying before. Are we adding an extra object to n ordered objects that are labeled {1,...,n} in such a way that it is still distinguishable from another object (an integer, presumably) of the same value? Or are we actually dealing with the integers 1,...,n themselves? That's what I assumed at first. Thank you.
 
  • #7
The final transformation is to realize
A set of m elements in {1, ..., n} along with an integer k such that r of the chosen elements are less than or equal to k​
contains the same data as
A set of m+1 elements in {1, ..., n+1}/indent]

To convert from the second to the first, let k be the r+1-th number (in sorted order). Remove k from the original list, and subtract one from k and from every chosen number bigger than k.

To convert from the first to the second, add one to every chosen number greater than k, then add k+1 to the list of chosen numbers.


Maybe balls in boxes will make it more clear. From the data
A set of m elements in {1, ..., n} along with an integer k such that r of the chosen elements are less than or equal to k​
we can depict it by lining up n boxes and placing m white balls into the boxes. Then insert one extra box between k and k+1 with a red ball.​
 
  • #8
Thank you, Hurkyl, for taking the time to explain it in detail. That makes sense now.

EDIT:

BTW, the above identity allows one to prove Laplace's celebrated (and abused) Rule of Succession without having to take limits.

An urn contains n balls. The number k of red balls is determined randomly so that the possibilities k = 0, 1, ..., n, are equally likely. The remaining n-k balls are white. You do not know the fraction of red balls. You draw m balls, r of which are red. What is the probability that the (m+1)st is red? Conditional on k, the probability that r of the first m, as well as the (m+1)st balls are red is

[tex]\frac{\binom{k}{r}\binom{n-k}{m-r}}{\binom{n}{m}}\frac{k-r}{n-m} = \frac{r+1}{m+1}\frac{\binom{k}{r+1}\binom{n-k}{(m+1)-(r+1)}}{\binom{n}{m+1}}[/tex]

Summing over k gives

[tex]\frac{r+1}{m+1}\frac{\binom{n+1}{m+2}}{\binom{n}{m+1}}[/tex]

which is proportional to the unconditional probability that r of the first m as well as the (m+1)st balls are red. The constant of proportionality is P(k) = 1/(n+1). Similarly, the unconditional probability that r of the first m balls are red is proportional to

[tex]\frac{\binom{n+1}{m+1}}{\binom{n}{m}}[/tex] by the same constant P(k) = 1/(n+1).

Finally, divide the first by the second and apply Bayes' theorem to get

[tex]P{\text{(m+1)st ball is red} | \text{r of the first m were red}} = \frac{(r+1)\binom{n}{m}\binom{n+1}{m+2}}{(m+1)\binom{n+1}{m+1}\binom{n}{m+1}} = \frac{r+1}{m+2}[/tex]

So, of course, if you flip a coin once and it comes up heads, that means it comes up heads on the next flip with probability 2/3! :biggrin:
 
Last edited:

What is combinatorial interpretation?

Combinatorial interpretation is a method used in mathematics and computer science to explain the meaning behind mathematical expressions and formulas. It involves interpreting mathematical objects and operations in terms of real-world scenarios or concepts.

How is combinatorial interpretation used in mathematics?

Combinatorial interpretation is often used in combinatorics, a branch of mathematics that deals with counting and arranging objects. It helps to provide a deeper understanding of the mathematical concepts and can also be used to prove identities or solve problems.

What are some examples of combinatorial interpretation?

Some examples of combinatorial interpretation include interpreting binomial coefficients as ways to choose a certain number of objects from a larger set, or interpreting permutations as arrangements of objects in a specific order.

How does combinatorial interpretation relate to computer science?

In computer science, combinatorial interpretation is often used in algorithms and data structures. It can help in designing efficient algorithms for tasks such as sorting, searching, and graph traversal. It can also be used in cryptography, where combinatorial interpretations of various mathematical operations are used to create secure encryption algorithms.

What are the benefits of using combinatorial interpretation?

Combinatorial interpretation can provide a more intuitive understanding of mathematical concepts and make them easier to visualize. It can also aid in problem-solving and can be used to prove mathematical identities. Additionally, combinatorial interpretation can have practical applications in various fields, such as computer science and economics.

Similar threads

  • Set Theory, Logic, Probability, Statistics
Replies
10
Views
965
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
835
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
2K
  • Set Theory, Logic, Probability, Statistics
Replies
3
Views
1K
  • Calculus and Beyond Homework Help
Replies
15
Views
1K
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
767
  • Calculus and Beyond Homework Help
Replies
6
Views
477
  • Set Theory, Logic, Probability, Statistics
Replies
1
Views
1K
  • Precalculus Mathematics Homework Help
Replies
11
Views
1K
Back
Top