Interpret Probability Distribution: Discrete Probability

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Discussion Overview

The discussion revolves around the interpretation of a specific probability distribution expressed as a summation involving binomial coefficients, probabilities of success, and trials. Participants explore its relationship to known distributions, particularly the negative binomial and geometric distributions, and seek to clarify its probabilistic meaning.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification

Main Points Raised

  • One participant questions how to interpret the given probability distribution and suggests it resembles a negative binomial distribution when manipulated.
  • Another participant seeks clarification on the value of the summation and its probabilistic interpretation.
  • A third participant proposes that the summation can be expressed as an expected value, indicating multiple ways to represent it, including connections to both negative binomial and geometric distributions.
  • There is a suggestion that while one might want to relate the two distributions through expected values, such conclusions may not be valid.

Areas of Agreement / Disagreement

Participants express varying interpretations of the distribution and its connections to other probability distributions, indicating that multiple competing views remain without a consensus on a definitive interpretation.

Contextual Notes

There are unresolved aspects regarding the assumptions underlying the manipulation of the distribution and the conditions under which the interpretations hold. The relationship between the distributions mentioned is not fully established.

hoffmann
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how do i interpret this probability distribution:

[tex]\sum_{k=r}^\infty \binom{k}{r}p^k(1-p)^{k-r}[/tex]

where r is the number of successes, p is the probability, k trials.

by looking at it, it seems like it's similar to a negative binomial distribution once you pull out a k/r. if you do some math after pulling out the k/r, it seems like it is the expected value of a geometric distribution. is this distribution saying that a negative binomial divided by the number of successes r means there is only one success, which is geometric?
 
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in other words, what does this sum equal?
 
hoffmann said:
how do i interpret this probability distribution ... it seems like it's similar to a negative binomial distribution once you pull out a k/r

Do you mean to ask "what is a probabilistic interpretation of this summation formula?" - in which case there would be lots of different ways to write it as an expected value E[f(X)] = sum(f(k)*Prob[X=k]). One way you found was with negative binomial X and f(X)=X/r; the other way was E[g(Y)] for some function g where Y is geometric.

However it sounds like you want to use E[f(X)]=E[g(Y)] to draw conclusions about how X and Y are related, but that's just not possible.

Does that help?
 
makes sense. thanks!
 

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