Expected value of joint distribution

In summary: You are a life saver.In summary, the expected value of X+Y can be found by adding the expected values of X and Y, which can be calculated by integrating over the given probability density function. After correcting for a calculation mistake, the final expected value is found to be 2/lambda. This makes more sense since X and Y are positive and therefore the expected value should also be positive.
  • #1
mrkb80
41
0

Homework Statement


Suppose that [itex]f_{X,Y}(x,y)=\lambda^2e^{-\lambda(x+y)},0\le x,0\le y [/itex]
find [itex]E[X+Y][/itex]

Homework Equations


The Attempt at a Solution



I just want to double check I didn't make a mistake:
[itex]E[X+Y]=E[X]+E[Y]=\int_0^{\infty} x{\lambda} e^{-\lambda x} dx + \int_0^{\infty} y{\lambda} e^{-\lambda y} dy = -2 - \dfrac{2}{\lambda} [/itex]
 
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  • #2
mrkb80 said:

Homework Statement


Suppose that [itex]f_{X,Y}(x,y)=\lambda^2e^{-\lambda(x+y)},0\le x,0\le y [/itex]
find [itex]E[X+Y][/itex]


Homework Equations





The Attempt at a Solution



Pretty sure I have this one right, I just want to double check I didn't make a calculation mistake:
[itex]E[X+Y]=E[X]+E[Y]=\int_0^{\infty} x{\lambda} e^{-\lambda x} dx + \int_0^{\infty} y{\lambda} e^{-\lambda y} dy = -2 - \dfrac{2}{\lambda} [/itex]
Since [itex]0\le x,0\le y [/itex], it's hard to think that the expected value could be negative. Try writing out your last step in detail.
 
  • #3
good point. I think I see my mistake(s):[itex] E[X] + E[Y]=\int_0^{\infty} x \lambda e^{-\lambda x} dx + \int_0^{\infty} y \lambda e^{-\lambda y} dy = - x \dfrac{1}{\lambda} e^{-\lambda x} |_0^{\infty} - \int_0^{\infty} e^{- \lambda x } dx - y \dfrac{1}{\lambda} e^{-\lambda y} |_0^{\infty} - \int_0^{\infty} e^{- \lambda y } dy =\dfrac{1}{\lambda} + \dfrac{1}{\lambda} = \dfrac{2}{\lambda}[/itex]
 
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  • #5
many thanks, by the way.
 

Related to Expected value of joint distribution

1. What is the expected value of a joint distribution?

The expected value of a joint distribution is the sum of the products of the possible outcomes and their probabilities. It represents the average value that we would expect to obtain if we were to repeat the experiment multiple times.

2. How is the expected value of a joint distribution calculated?

The expected value of a joint distribution is calculated by multiplying each possible outcome by its corresponding probability and then summing all of these products together.

3. What is the purpose of calculating the expected value of a joint distribution?

The expected value of a joint distribution is used to analyze and understand the behavior of random variables. It helps us to make predictions about the likelihood of certain outcomes and make informed decisions based on these predictions.

4. Can the expected value of a joint distribution be negative?

Yes, the expected value of a joint distribution can be negative if the possible outcomes have negative values and their probabilities are high enough to offset the positive outcomes.

5. How is the expected value of a joint distribution different from the expected value of a single variable?

The expected value of a joint distribution takes into account the probabilities of multiple variables occurring together, while the expected value of a single variable only considers the probabilities of that one variable. In other words, the expected value of a joint distribution is a measure of the overall behavior of multiple variables, while the expected value of a single variable is a measure of that variable's behavior on its own.

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