Finding expectation of the function of a sum

In summary, the answer to the original question is that the expected value of the sine is zero if the random variable ##S_n## has a discrete distribution.
  • #1
WMDhamnekar
MHB
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TL;DR Summary
Finding expectation of the function of a sum of i.i.d. random variables given the 2nd moment of sum of i. i. d. random variables.
1678781098515.png

My answer:
1678781001557.png


Is the above answer correct?
 
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  • #2
##S_n$$ has a discrete distribution. How did you get continuous?
 
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  • #3
Oh, I am sorry. You are correct. So, if ## S_n## has a discrete distribution, my answer would be
* ##S_n## is a random walk with symmetric steps, so ##E(S_n) = 0##
* ##S_n^2## is the sum of n independent random variables taking values 1 and -1 with equal probability, so ##E(S_n^2) = n##
* By conditional expectation, ##E(\sin{S_n} | S_n^2) = E(\sin{S_n} | n) = \frac{E(\sin{S_n})}{E(n)} = \frac{0}{n} = 0##.
 
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  • #4
I don't think you're handling the conditional part right. Your final answer is supposed to be a function of ##S_n^2## - given a specific value that it ends up being, what is the expected value of the sine?
 
  • #5
Office_Shredder said:
I don't think you're handling the conditional part right. Your final answer is supposed to be a function of ##S_n^2## - given a specific value that it ends up being, what is the expected value of the sine?
I edited my answer to this question. Does it look now correct?
 
  • #6
No, I still think you have written meaningless notation.

Try to compute ##E(\sin(S_n) | S_n)## what does this even mean?
 
  • #7
Office_Shredder said:
No, I still think you have written meaningless notation.

Try to compute ##E(\sin(S_n) | S_n)## what does this even mean?
##S_n= \pm\sqrt{n}## with equal probability ##\frac12 \therefore E(\sin{(S_n)}|S^2_n)=\sin{(S_n=0)}\times \frac12 - \sin{(S_n=0)}\times \frac12 =0##
 
  • #8
Here's what I think the solution is to my problem. I'm going to add two characters to make the notation more clear.

##E(\sin(S_n)|S_n=x)##. The expected value of the sine, given ##S_n## begs the question, given ##S_n## what? Given ##S_n## means given the realized outcome of ##S_n## from the random trial. I've decided to call that outcome ##x##.

So if someone tells you they sampled all the random variables and this time ##S_n=3## what is he expected value of ##\sin(S_n)##? Hopefully the answer is obvious, ##\sin(3)##. It's the only possible value of ##\sin(S_n)##. If we want to be very formal we could write this as ##E(\sin(S_n)|S_n=3)=\sum_y \sin(y) P(S_n=y | S_n=3)##. (In fact this is what I know as the definition of this notation) Obviously ##P(S_n=y|S_n=3)## is 0 except for ##y=3## where it's 1.

There's nothing special about 3, in general ##E(\sin(S_n) | S_n=x)=\sin(x)##.

Now in your original problem, being told ##S_n^2=16##, to pick a random possible number, doesn't restrict ##S_n## to only a single value. But there aren't that many choices for what it can be. If looks in your last post like you were hitting on this, but I don't know why you wrote ##\sin(S_n=0)## for example, or what that even means. Also, ##S_n^2## is very unlikely to actually equal ##n## exactly.
 
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1. What is the formula for finding the expectation of the function of a sum?

The formula for finding the expectation of the function of a sum is E[f(X+Y)] = E[f(X)] + E[f(Y)], where X and Y are random variables and f is a function.

2. How is the expectation of the function of a sum different from the expectation of a sum of functions?

The expectation of the function of a sum involves finding the expectation of a single function applied to the sum of two random variables, while the expectation of a sum of functions involves finding the sum of the expectations of each individual function applied to the random variables.

3. Can the expectation of the function of a sum be calculated if the random variables are not independent?

Yes, the expectation of the function of a sum can still be calculated even if the random variables are not independent. However, the formula may be more complex and may involve the covariance between the two random variables.

4. How can the expectation of the function of a sum be used in practical applications?

The expectation of the function of a sum is commonly used in statistics and probability to analyze and make predictions about the behavior of random variables. It can also be used in fields such as finance and engineering to model and analyze complex systems.

5. Are there any limitations or assumptions when using the expectation of the function of a sum?

One limitation is that the formula assumes that the random variables are continuous and have a well-defined probability density function. Additionally, the formula may not be applicable if the random variables have a highly skewed distribution or if the function being applied is not well-behaved.

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