Bivariate expected value and variance

In summary, the conversation is about a student struggling to understand and verify formulas for bivariate probability density functions. They discuss the expected value and variance for both x and (x,y), as well as whether the book means E[XY] or E[X,Y]. The student also notes that their book only has formulas for Var[X+Y] and not Var[X,Y]. The other person confirms that the student has the correct formula for E[X] and Var[X].
  • #1
ArcanaNoir
779
4

Homework Statement



I need to know these formulas to answer the homework problems, but I can't squeeze the forumlas out of the gibberish in the book, so I'm asking for varification of the formulas.

For a bivariate probablity density function, for example f(x,y)= 2xy when x and y are between 0 and 3, and 0 elsewhere,

expected value of x: E[X]
expected value of (X,Y)
Variance of x: Var[X]
Var[X,Y]


[tex] E[x] = \int_{-\infty }^{\infty }xf_1(x) \: \mathrm{d}x [/tex] where [itex] f_1(x) [/itex] is the marginal probability distribution of x. Is this correct?

Now, for E[X,Y], do you think the book means the expected value of the product XY? because the only formula it gives here is for the product. So, E[XY]. If they really mean E[X,Y] and not E[XY], then is there a formula for E[X,Y]? I don't have one in my book.

As for Var[X], is it [tex] Var[x] = \int_{-\infty }^{\infty }x^2f_1(x) \: \mathrm{d}x -(E[x])^2 [/tex] ?

And for Var[X,Y], I have no idea, the only formulas I see in my book are for Var[X+Y].

Remember, these are all for bivariate distributions.
 
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  • #2
E[X,Y] would be (E[X],E[Y]).
Same for Var[X,Y].

You have the right E[X] and Var[X].
 
  • #3
Thanks
 

FAQ: Bivariate expected value and variance

1. What is bivariate expected value?

Bivariate expected value refers to the average value that is expected to be obtained when two random variables are taken into consideration. It is calculated by multiplying each possible value of one variable by the corresponding probability of that value and then summing up all the products.

2. How is bivariate expected value different from univariate expected value?

Univariate expected value only takes into account one random variable, while bivariate expected value considers two random variables simultaneously. This means that bivariate expected value takes into account the relationship between the two variables and provides a more accurate measure of expected value.

3. What is the formula for calculating bivariate expected value?

The formula for calculating bivariate expected value is E(X,Y) = ∑∑x_i * y_i * p(x_i, y_i), where x_i and y_i are the possible values of the two variables and p(x_i, y_i) is their joint probability.

4. What is bivariate variance?

Bivariate variance is a measure of the spread or variability of the joint distribution of two random variables. It takes into account the covariance between the two variables and provides a measure of how much they vary together.

5. How is bivariate variance calculated?

Bivariate variance can be calculated using the formula Var(X,Y) = E[(X-E(X))(Y-E(Y))], where E(X) and E(Y) are the expected values of the two variables. Alternatively, it can also be calculated using the formula Var(X,Y) = Cov(X,Y) = E(XY) - E(X)E(Y), where Cov(X,Y) is the covariance between the two variables.

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