Poisson Distribution: Find E[N ∑Nᵢ₁Xᵢ]

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Homework Help Overview

The problem involves calculating the expected value of a sum of independent random variables where the number of terms in the sum is itself a random variable following a Poisson distribution. The original poster presents a scenario with independent random variables N and X_i, where N has a Poisson distribution with mean 3 and each X_i has a Poisson distribution with mean 7.

Discussion Character

  • Exploratory, Mathematical reasoning, Assumption checking

Approaches and Questions Raised

  • Participants discuss the application of properties of expectations, including the use of the tower law and conditioning on N. There are attempts to express the expected value in terms of E[N] and E[X]. Some participants also explore the calculation of E[N^2] and its implications for the overall expectation.

Discussion Status

The discussion includes various approaches to the problem, with some participants providing calculations for E[N^2] and suggesting methods for conditioning on N. There is no explicit consensus on the correct approach, but multiple interpretations and methods are being explored.

Contextual Notes

Participants note the need to clarify assumptions regarding the distributions of the random variables and the implications of using properties of expectations in this context. There is also mention of a second part of the question regarding the variance of the sum, which adds complexity to the discussion.

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Homework Statement



Let N,X1, X2, ... be independent random bariables where ?N has a poission Distribution with mean 3 while X1, X2... each has a poisson distribution with mean 7

Determine [tex]E[N \sum^N_{i=1} X_i][/tex]

Homework Equations





The Attempt at a Solution



[tex]E[N \sum^N_{i=1} X_i] = E[N] * E[\sum^N_{i=1} X_i] = (3)(7) = 21[/tex]

but that can't be right.
 
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Hi cse63146

I would first look at for constant sum of m Xi's, with the Xi's representing the same distribution X independently, the sum gives:
[tex]E[ \sum^m_{i=1} X_i] = *E[X_1 + X_2 + ... + X_m ] = m*E[X][/tex]
 
so the expectation would be E[N2]*E[X]

E[N2] = Var(x) + E[N]2 - 3 + 32 = 12

E[N2]*E[X] = (12)(7) = 84
 
You may want to use the tower law
[tex]\mathbb{E}[X]=\mathbb{E}[\mathbb{E}[X|Y]][/tex]

You need to condition on the N to pull out the sum.
 
Let T = X1 + X2 + XN. E[T|N] = E[X1 + X2 + XN | N ]

E[T|N] = E[X1 + X2 + XN] = N*E[X]

E[T] = E[E[T|N]] = E[NE[X]] = E[N]*E[X]

[tex]E[N \sum^N_{i=1} X_i] = E[N^2]E[X][/tex]

E[N2] = Var(x) + E[N2] = 3 + 32 = 12

E[N2]*E[X] = (12)(7) = 84.

There's a second part of the question - Determine the variance of

[tex]\sum^N_{i=1} X_i[/tex]

so Var (T|N) = Var(X1+ ... + Var(XN). Since X1 X2 XN are independent: Var(T|N) = N*Var(X).

Is that correct?
 

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