Multivariable Probability Distribution

In summary: RGVIn summary, we discussed finding the value of k in f(x,y) = ke^(-x-y) to make it a probability density function on the given domain. We then set up the double integral and discussed the correct bounds for the variables. We also found the marginal distributions of X and Y and calculated the conditional distributions. After some corrections, we arrived at the final correct expressions for the marginal and conditional distributions.
  • #1
mathmajor23
29
0
f(x,y) = ke^(-x-y), 0<x<∞ , 0<y<∞ , and x<y

Find k to make this a PDF.

So I set up: ∫(from 0 to ∞)∫(from 0 to ∞) [ke^(-x-y)]dxdy. Is this integral right?

I also need to find the marginals of X and Y but my k has to be right first.
 
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  • #2
mathmajor23 said:
f(x,y) = ke^(-x-y), 0<x<∞ , 0<y<∞ , and x<y

Find k to make this a PDF.

So I set up: ∫(from 0 to ∞)∫(from 0 to ∞) [ke^(-x-y)]dxdy. Is this integral right?

I also need to find the marginals of X and Y but my k has to be right first.

I am not familiar with multi-variable, but if it is like single, you would need to set the integral = 1 and you could find your k.
 
  • #3
Dustinsfl said:
I am not familiar with multi-variable, but if it is like single, you would need to set the integral = 1 and you could find your k.

Yes, it's the same as single variable. I'm just unsure on my bounds of my integrals.
 
  • #4
mathmajor23 said:
Yes, it's the same as single variable. I'm just unsure on my bounds of my integrals.

[0,Infinity) seem reasonable.
 
  • #5
mathmajor23 said:
f(x,y) = ke^(-x-y), 0<x<∞ , 0<y<∞ , and x<y

Find k to make this a PDF.

So I set up: ∫(from 0 to ∞)∫(from 0 to ∞) [ke^(-x-y)]dxdy. Is this integral right?

I also need to find the marginals of X and Y but my k has to be right first.

Your integration is over the two-dimensional set {(x,y}: 0 ≤ x < ∞, 0 ≤ y < ∞}, but you told us that f is defined only for x < y.

RGV
 
  • #6
Ray Vickson said:
Your integration is over the two-dimensional set {(x,y}: 0 ≤ x < ∞, 0 ≤ y < ∞}, but you told us that f is defined only for x < y.

RGV

Oops! So it should be ∫from 0 to ∞ ∫ from 0 to x [f(x,y)dxdy] ?
 
  • #7
mathmajor23 said:
Oops! So it should be ∫from 0 to ∞ ∫ from 0 to x [f(x,y)dxdy] ?

Yes, or the other way if you prefer (y from 0 to ∞ and x from 0 to y).

RGV.
 
  • #8
What's the integral of the inner part evaluate to? I tried using integration by substitution but it's not working.
 
  • #9
mathmajor23 said:
What's the integral of the inner part evaluate to? I tried using integration by substitution but it's not working.

Are you saying you don't know how to do the integral [itex] \int_0^x e^{-y} \, dy?[/itex] I hope you are joking.

RGV
 
  • #10
I got that k=2, and checked it by seeing it integrated to 1.

For the Marginal Distributions of X and Y, I got :
f(x) = 2[e^(-x)-e^(-2x)] I [0,infinity) (x)
f(y) = 2[e^(-y)-e^(-2y)] I [0,infinity) (y)

Then, for the conditionals, I got:
f(x|y) = e^(-x) / [1-e^(-y)] I[0,infinity) (x)
f(y|x) = e^(-y) / [1-e^(-x)] I[0,infinity) (y)

Can someone check those? Thanks!
 
  • #11
mathmajor23 said:
I got that k=2, and checked it by seeing it integrated to 1.

For the Marginal Distributions of X and Y, I got :
f(x) = 2[e^(-x)-e^(-2x)] I [0,infinity) (x)
f(y) = 2[e^(-y)-e^(-2y)] I [0,infinity) (y)

Then, for the conditionals, I got:
f(x|y) = e^(-x) / [1-e^(-y)] I[0,infinity) (x)
f(y|x) = e^(-y) / [1-e^(-x)] I[0,infinity) (y)

Can someone check those? Thanks!

Your marginal for y is correct but your marginal for x is incorrect. Do NOT use the same f for three different functions. You already used f for f(x,y), so call the marginals something different, like g(x) and h(y), or fX(x) and fY(y).

RGV
 
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  • #12
Was my conditional of f(x|y) correct?

Hmm is this the MD of x then?

Integral from 0 to infinity [2e^(-x-y)dy]
= -2(e^(-x-y)] from y=0 to y=infinity
=2[1+e^(x)] I [0,infinity) (x)
 
Last edited:
  • #13
mathmajor23 said:
Was my conditional of f(x|y) correct?

Hmm is this the MD of x then?

Integral from 0 to infinity [2e^(-x-y)dy]
= -2(e^(-x-y)] from y=0 to y=infinity
=2[1+e^(x)] I [0,infinity) (x)

There is no point in answering questions about conditional densities until you get both marginal densities correct. The integral above is wrong.

RGV
 
  • #14
Which part of f(x) = 2[e^(-x)-e^(-2x)] I [0,infinity) (x) is wrong?
 
  • #15
mathmajor23 said:
Which part of f(x) = 2[e^(-x)-e^(-2x)] I [0,infinity) (x) is wrong?

All of it. You seem to have claimed
[tex] \int_x^{\infty} e^{-x-y}\, dy = e^{-x} - e^{-2x}.[/tex]
but this is absolutely not correct.

RGV
 
  • #16
Let's start over.

I originally had for the marginal of X:
∫ from 0 to x of [2e^(-x-y)]dy
=-2[e^(-x-y)], evaluated from y=0 to y=x
= 2[e^(-x)-e^(-2x)] I[0,∞) (x)

To check this, I integrated this and it came out to 1, so I'm not sure why this is wrong.
 
  • #17
mathmajor23 said:
Let's start over.

I originally had for the marginal of X:
∫ from 0 to x of [2e^(-x-y)]dy
=-2[e^(-x-y)], evaluated from y=0 to y=x
= 2[e^(-x)-e^(-2x)] I[0,∞) (x)

To check this, I integrated this and it came out to 1, so I'm not sure why this is wrong.

It is wrong because you integrated y from 0 to x instead of from x to infinity.

RGV
 
  • #18
Ray Vickson said:
It is wrong because you integrated y from 0 to x instead of from x to infinity.

RGV
Ok, hopefully here's the final check:

fx(x) =
∫ from x to infinity [2e^(-x-y)]dy
=-2[e^(-x-y)] from y=x to y=∞
= 2e^(-2x) I[0,∞) (x)

fy(y) = 2[e^(-y)-e^(-2y)] I [0,infinity) (y)

f(x|y) = 2e^(-x-y) / 2[e^(-y)-e^(-2y)]
= [e^(-x)] / (1-e^(-y)) I[0,∞) (x)

f(y|x) = 2e^(-x-y) / 2e^(-2x)
= e^(-y) / e^(-x) I[0,∞) (y)
 
  • #19
mathmajor23 said:
Ok, hopefully here's the final check:

fx(x) =
∫ from x to infinity [2e^(-x-y)]dy
=-2[e^(-x-y)] from y=x to y=∞
= 2e^(-2x) I[0,∞) (x)

fy(y) = 2[e^(-y)-e^(-2y)] I [0,infinity) (y)

f(x|y) = 2e^(-x-y) / 2[e^(-y)-e^(-2y)]
= [e^(-x)] / (1-e^(-y)) I[0,∞) (x)

f(y|x) = 2e^(-x-y) / 2e^(-2x)
= e^(-y) / e^(-x) I[0,∞) (y)

These look OK now, except you should get rid of those annoying I[0,∞)(x), etc., symbols. Just say in words that x and y are >= 0.

RGV
 
  • #20
Thanks a bunch!

I, too, hate the annoying indicator functions, but my professor seems the need to "require" us to use them.
 

What is a multivariable probability distribution?

A multivariable probability distribution is a mathematical function that describes the probabilities of different outcomes occurring in a system with multiple variables. It takes into account the relationships between these variables and assigns a probability to each possible outcome.

How is a multivariable probability distribution different from a univariable probability distribution?

A univariable probability distribution only considers one variable and its possible outcomes, while a multivariable probability distribution takes into account multiple variables and their relationships to each other.

What is the purpose of using a multivariable probability distribution?

A multivariable probability distribution is used to model and analyze complex systems that involve multiple variables. It allows for a more accurate understanding of the probabilities of different outcomes and can help with decision making and risk assessment.

What are some common types of multivariable probability distributions?

Some common types of multivariable probability distributions include the bivariate normal distribution, multivariate normal distribution, and joint distribution. These distributions are often used in statistics, economics, and other fields to model real-world scenarios.

How is a multivariable probability distribution represented and calculated?

A multivariable probability distribution is typically represented using mathematical notation, such as a probability density function or a joint probability function. It can be calculated using various methods, including integration and simulation techniques.

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