Avg. of sum of independent variables

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SUMMARY

The discussion focuses on calculating the average of the sum of independent random variables, specifically using the probability density functions (PDFs) of each variable. The key equation derived is = n , where represents the expected value of each independent variable. The integration of the joint probability distribution P(X_1, X_2, ..., X_n) is essential for expressing P(Y) as a function of a single variable. This method confirms that the assumption of independence among the variables is valid for the calculations presented.

PREREQUISITES
  • Understanding of probability density functions (PDFs)
  • Familiarity with integration techniques in multivariable calculus
  • Knowledge of expected value and its properties
  • Basic concepts of independent random variables
NEXT STEPS
  • Study the properties of joint probability distributions
  • Learn about the Central Limit Theorem and its implications for sums of random variables
  • Explore the concept of convolution in probability theory
  • Investigate the use of delta functions in probability distributions
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Students in statistics or probability theory, researchers in quantitative fields, and anyone interested in understanding the behavior of sums of independent random variables.

Pushoam
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Homework Statement


upload_2017-9-11_10-15-19.png


Homework Equations

The Attempt at a Solution


The probability that ##X_1 ## is between ## X_1 ## and ## X_1 + dX_1 ## and ##X_2 ## is between ## X_2 ## and ## X_2 + dX_2 ## and so on till the nth variable is
dP(##X_1,
X_2, ..., X_n) = p ( X_1) p( x_2) p(X_3)...p(X_n) dX_1 dX_2 ...dX_n
\\ dP(Y) = p(Y) dY
\\<Y>= \int Yp(Y) dY
\\ assuming dP(y) =
dP(X_1,
X_2, ..., X_n)
\\= \int ( X_1 +X_2 + ... + X_n) p ( X_1) p( x_2) p(X_3)...p(X_n) dX_1 dX_2 ...dX_n
\\ = \int X_1 p ( X_1)dX_1+ \int X_2 p ( X_2)dX_2+ ...+\int X_n p ( X_n)dX_n
\\= <X_1> + <X_2>+...+<X_n>
\\= n <X>##
Is the assumption o.k?
 
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You are never really using that assumption, you are using
$$
\langle Y \rangle = \int Y(x_1,\ldots,x_n) P(x_1,\ldots, x_n) dx_1 \ldots dx_n.
$$
If you want to express ##P(Y)## as a one-variable function you need to integrate out the ##X_i## variables, essentially
$$
P(Y) = \int \delta(Y-x_1-\ldots-x_n) P(x_1,\ldots,x_n) dx_1 \ldots dx_n.
$$
Note that this leads to
$$
\langle f(Y)\rangle = \int f(y) P(y) dy = \int f(y) \delta(y-x_1-\ldots-x_n) P(x_1,\ldots,x_n) dx_1 \ldots dx_n dy
= \int f(x_1+\ldots+x_n) P(x_1,\ldots,x_n) dx_1 \ldots dx_n,
$$
so it is compatible with the original probability distribution.
 
Thank you for this insight.
 

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