Convolution of two geometric distributions

Click For Summary

Discussion Overview

The discussion centers on deriving the convolution of two geometric distributions, specifically examining the summation involved in calculating the probability mass function of the sum of two independent geometric random variables. Participants explore the correct formulation of the summation and the implications of the starting index for the summation.

Discussion Character

  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant presents a summation for the convolution of two geometric distributions, questioning the correctness of their result.
  • Another participant suggests a potential error in the formulation of the summation, proposing an alternative expression.
  • A third participant references an external source to clarify the correct limits of the summation, indicating that the starting index should be adjusted.
  • Further clarification is provided regarding the definition of the geometric distribution and the implications for the summation limits, emphasizing that the smallest value of the sum should start from 2.
  • Another participant acknowledges the previous points and agrees that the summation should indeed start from 2, leading to a revised expression for the probability.
  • The discussion includes references to specific probabilities for small values of the sum, questioning whether the derived formulas align with these probabilities.

Areas of Agreement / Disagreement

Participants express differing views on the correct formulation of the summation and the appropriate starting index. There is no consensus reached on the final expression, as multiple competing views remain regarding the correct approach to the convolution.

Contextual Notes

Participants highlight the importance of correctly defining the limits of summation in the context of geometric distributions, noting that the assumptions about the starting index significantly affect the outcome. There are unresolved mathematical steps regarding the derivation of the convolution.

Ad VanderVen
Messages
169
Reaction score
13
TL;DR
I am trying to derive the convolution of two geometric distributions but obviously I am making an error.
I'm trying to derive the convolution from two geometric distributions, each of the form:
$$\displaystyle \left( 1-p \right) ^{k-1}p$$ as follows $$\displaystyle \sum _{k=1}^{z} \left( 1-p \right) ^{k-1}{p}^{2} \left( 1-p \right) ^{z-k-1}.$$ with as a result: $$\displaystyle \left( 1-p \right) ^{z-2}{p}^{2}z$$ Now the sum: $$\displaystyle \sum _{z=2}^{\infty } \left( 1-p \right) ^{-2+z}{p}^{2}z.$$ should be equal to ##\displaystyle \frac{2}{p}## which is not the case.

What am I did wrong?
 
Last edited:
Physics news on Phys.org
$$\displaystyle \sum _{k=1}^{z} \left( 1-p \right) ^{k-1}{p}^{2} \left( 1-p \right) ^{z-k-1}$$ seems strange. You sure it's not $$\displaystyle \sum _{k=1}^{z} \left( 1-p \right) ^{k-1}{p}^{2} \left( 1-p \right) ^{z-k+1}\ ? $$
 
Ad VanderVen said:
I'm trying to derive the convolution from two geometric distributions, each of the form:
$$\displaystyle \left( 1-p \right) ^{k-1}p$$
That indicates that you define the geometric distribution at ##k## to be the probability of success on the ##k##-th attempt, as oppose to the probability of success after ##k## failures.

So , as noted on the stackexchange link, if you consider the sum of two geometric random variables, the smallest possible value of ##k## is 2, which results when you succeed on the 1st try from the first distribution and again on the first try from the second distribution. So it isn't clear why you are writing a summation beginning with ##k = 1##.
as follows $$\displaystyle \sum _{k=1}^{z} \left( 1-p \right) ^{k-1}{p}^{2} \left( 1-p \right) ^{z-k-1}.$$
 
Stephen Tashi said:
So , as noted on the stackexchange link, if you consider the sum of two geometric random variables, the smallest possible value of ##k## is 2, which results when you succeed on the 1st try from the first distribution and again on the first try from the second distribution. So it isn't clear why you are writing a summation beginning with ##k = 1##.
I'm afraid you're absolutely right. Only then will you get $$\displaystyle P \left( X+Y=z \right) \, = \,\sum _{k=2}^{z} \left( 1-p \right) ^{k-1}{p}^{2} \left( 1-p \right) ^{z-k-1}$$
 
Ad VanderVen said:
The case is treated at: https://math.stackexchange.com/ques...of-two-independent-geometric-random-variables and the summation:$$\displaystyle \sum _{k=1}^{z} \left( 1-p \right) ^{k-1}{p}^{2} \left( 1-p \right) ^{z-k-1}$$ must be$$\displaystyle \sum _{k=1}^{z-1} \left( 1-p \right) ^{k-1}{p}^{2} \left( 1-p \right) ^{z-k-1}.$$

That formula comes from the message on stackexchange that asked the question and conjectured a formula. Look at the formulas given by the messages that answered the question.

Ad VanderVen said:
$$\displaystyle P \left( X+Y=z \right) \, = \,\sum _{k=2}^{z} \left( 1-p \right) ^{k-1}{p}^{2} \left( 1-p \right) ^{z-k-1}$$

##P(X+Y =2) = p^2##, Does the above formula agree with that when ##z = 2##?

##P(X+Y = 3) = p(1-p)p + (1-p)pp = 2(1-p)p^2 ##
 

Similar threads

  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 12 ·
Replies
12
Views
2K
  • · Replies 25 ·
Replies
25
Views
3K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 5 ·
Replies
5
Views
3K
  • · Replies 7 ·
Replies
7
Views
2K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 1 ·
Replies
1
Views
1K
  • · Replies 1 ·
Replies
1
Views
3K