Expected value and equality to sums

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SUMMARY

The discussion focuses on demonstrating the equality of the expected value of a non-negative integer-valued random variable 'N' with the sums of probabilities, specifically showing that \(E[N] = \sum_{k=1}^\infty P\{N \geq k\} = \sum_{k=0}^\infty P\{N > k\}\). Participants shared insights on using indicator random variables \(I_n\) to express the expected value and provided useful identities related to binomial coefficients. The conversation emphasizes the importance of understanding these mathematical concepts for proper application in probability theory.

PREREQUISITES
  • Understanding of non-negative integer-valued random variables
  • Familiarity with probability notation and concepts
  • Knowledge of indicator random variables
  • Basic understanding of binomial coefficients and their properties
NEXT STEPS
  • Study the properties of indicator random variables in probability theory
  • Learn about the derivation of expected values for discrete random variables
  • Explore the use of binomial coefficients in probability and combinatorics
  • Investigate the relationship between cumulative distribution functions and expected values
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Mathematicians, statisticians, and students studying probability theory who seek to deepen their understanding of expected values and their derivations.

WMDhamnekar
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How to show that$E[N]=\displaystyle\sum_{k=1}^\infty P{\{N\geq k\}}=\displaystyle\sum_{k=0}^\infty P{\{N>k\}}$

If any member here knows the answer, may reply to this question.:confused:
 
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Dhamnekar Winod said:
How to show that$E[N]=\displaystyle\sum_{k=1}^\infty P{\{N\geq k\}}=\displaystyle\sum_{k=0}^\infty P{\{N>k\}}$

If any member here knows the answer, may reply to this question.:confused:
Hello,
'N' denote a non-negative integervalued random variable.
 
Dhamnekar Winod said:
Hello,
'N' denote a non-negative integervalued random variable.
Hello,

I got the answer after doing some carefully thinking.
 
Dhamnekar Winod said:
Hello,

I got the answer after doing some carefully thinking.

Perhaps yu'd like to share your solution so that others facing the same or similar question can benefit from your work?
 
Hello,
If we define the sequence of random variable $I_n$ (Indicator random variable), n > 1 by

$$I_n= \left \{ {1,\text{if n < X} \atop \text{0, if n>X}} \right.$$. Now express X in terms of $I_n.$ (Actually, I don't know how to express in terms of $I_n$:confused:)

I understood the equation in #1 by using the expectation of random variable X(outcome of a toss of a fair dice)is equal to summation of the probabilities of X > n, where range of n is 0 to $\infty$

I think the following below mentioned identities will be useful here.

$$ a)(1-1)^N= \left \{{\text{1, if N > 0}\atop \text{0, if n < 0}} \right.$$
$$b)(1-1)^N=\displaystyle\sum_{n=0}^n\binom{N}{i}*(-1)^i$$

$$ c)1-I=\displaystyle\sum_{n=0}^n\binom{N}{i}*(-1)^i$$

$$ d)I=\displaystyle\sum_{n=1}^n\binom{N}{i}*(-1)^i$$

If you want to show this equation in mathematical language, you may reply to that effect.:)
 
Last edited:

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