Combinatorics/Probability problem.

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In summary, the conversation discusses the problem of proving the formula for the sum of two binomial coefficients and its combinatorial interpretation. The speaker has been struggling with the problem for three days and has tried various approaches, including counting, using properties of binomial coefficients, and connecting it to Banach's matchbox problem and the Bernoulli triangle. The conversation ends with both parties unable to solve the problem.
  • #1
gumi_kr
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Homework Statement



Let [tex]R ^{M} _{P}= \sum_{s=0}^{P} {M+1 \choose s}[/tex], for [tex]0 \leqslant P \leqslant M [/tex], [tex]P,M\in \mathbb{N}[/tex].

Proove that:
[tex] \sum_{q=0}^{M}R^{M}_{q}\cdot R^{M}_{M-q}=(2M+1) {2M \choose M}[/tex]

and give it's combinatorical idea.

I'm trying to solve this for 3 days - please help..
 
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  • #2
What do you have so far? What have you tried?
 
  • #3
It was an usual exercise in probability theory course. It looks easy
but I'm trying to solve this for a long time without success.

1) I know that trying simply to count it - isn't the right way (even
using some advanced properties of binomial coefficient).2) It could be connected with Banach's modified matchbox problem, but
not neccessery (right side of the formula multiplied by 2^{n-1} is
expected number of matches..)

3) right sight looks like "choosing the leader and it's group" but i
cannot find connection with left side with this 'intuition'

4) typing R^{M}_{P} with Gamma and hypergeometric function is not a
good option - too many calculations

5) It could be connected with properties of 'Bernoulli triangle' but i
couldn't find any materials about that.
(it's The number triangle (Sloane's A008949) composed of the partial
sums of binomial coefficients)

6) I couldn't find anything useful in "Advanced Combinatorics"
(Comtet) or Combinatorics 2nd R. Merris
---
well, i can show you some easy calculations (but i don't think that it makes the problem easier):
[tex] \sum_{q=0}^{M}R^{M}_{q}\cdot R^{M}_{M-q}= \sum_{q=0}^{M}R^{M}_{q}\cdot (2^{M+1} - R_{q}^{M}) = 2^{M+1}\cdot \sum_{q=0}^{M}(M+1-q)\cdot {M+1 \choose q} - \sum_{q=0}^{M} (R^{M}_{q})^{2} = 2^{M+1}\cdot \sum_{q=0}^{M+1}q\cdot {M+1 \choose q} - \sum_{q=0}^{M} (R^{M}_{q})^{2} =[/tex]
[tex] =2^{2M+1}\cdot (M+1) - \sum_{q=0}^{M} (R^{M}_{q})^{2}[/tex]

On the other hand: (Banach's matchbox problem and it's expecting value):
[tex](2M+1)\cdot {2M \choose M} = 2^{2M} + \sum_{q=0}^{M} q\cdot {2M-q \choose M}\cdot 2^{q}[/tex]
 
Last edited:
  • #4
Well, I took a look and couldn't get any farther than you did, sorry! Definitely does not look easy ...
 

FAQ: Combinatorics/Probability problem.

What is combinatorics?

Combinatorics is a branch of mathematics that deals with counting and arranging objects or events. It helps in solving problems related to probability, statistics, and optimization.

What is the difference between permutation and combination?

Permutation is the arrangement of objects in a specific order, while combination is the selection of objects without considering their order. In permutation, the order matters, while in combination, it does not.

What is the formula for calculating permutations and combinations?

The formula for calculating permutations is nPr = n! / (n-r)!, where n is the total number of objects and r is the number of objects being arranged. The formula for combinations is nCr = n! / (r!(n-r)!), where n is the total number of objects and r is the number of objects being selected.

How is combinatorics used in real life?

Combinatorics is used in various fields such as computer science, finance, and engineering. It is used in coding and cryptography, stock market analysis, and circuit design, to name a few. It also helps in solving everyday problems such as making seating arrangements, creating schedules, and organizing events.

What is the role of probability in combinatorics?

Probability is closely related to combinatorics as it deals with the likelihood of an event occurring. Combinatorics helps in calculating the total number of possible outcomes, and probability helps in determining the chances of a specific outcome. It is used in games of chance, weather forecasting, and risk assessment.

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