Expected value and equality to sums

In summary, to show that $E[N] = \displaystyle\sum_{k=1}^\infty P\{N\geq k\} = \displaystyle\sum_{k=0}^\infty P \{N>k\}$, we can use the identities $a)$, $b)$, $c)$, and $d)$, along with the expectation of a random variable $X$, to express $N$ in terms of the indicator random variable $I_n$. This allows us to rewrite the equation in terms of probabilities and summations, providing a mathematical solution to the problem. It would be helpful to share this solution for others facing similar questions.
  • #1
WMDhamnekar
MHB
381
28
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:
 
Mathematics news on Phys.org
  • #2
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.
 
  • #3
Dhamnekar Winod said:
Hello,
'N' denote a non-negative integervalued random variable.
Hello,

I got the answer after doing some carefully thinking.
 
  • #4
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?
 
  • #5
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:

FAQ: Expected value and equality to sums

What is expected value?

The expected value, also known as the mean, is a measure of central tendency that represents the average outcome of a random variable. It is calculated by multiplying each possible outcome by its probability and summing them together.

How is expected value used in decision-making?

Expected value is commonly used in decision-making to evaluate the potential outcomes of different choices. By comparing the expected values of each option, one can make a more informed decision based on the potential risks and rewards.

What does equality to sums mean in relation to expected value?

Equality to sums means that the expected value of a sum of random variables is equal to the sum of their individual expected values. In other words, the expected value of a combination of events is the sum of the expected values of each event.

Can expected value be negative?

Yes, expected value can be negative. This typically occurs when the potential outcomes of a random variable have a higher probability of being negative. It is important to consider both positive and negative expected values in decision-making.

How does expected value relate to probability?

Expected value and probability are closely related. Probability represents the likelihood of a specific outcome occurring, while expected value takes into account both the probability and the potential outcome. In other words, expected value is the weighted average of all possible outcomes based on their probabilities.

Similar threads

Replies
3
Views
2K
Replies
1
Views
1K
Replies
1
Views
1K
Replies
2
Views
2K
Replies
14
Views
2K
Back
Top