Probability density function integral not converging

In summary, the conversation discusses the problem of determining whether the function f(x,y)=xe^{-xy} is a probability density function for x≥0 and y≥1. The necessary condition for a function to be a pdf is that the double integral over the specified bounds must equal 1. However, the integral is found to be not convergent, leading to confusion about the correct bounds and the discovery that the order of integration can be reversed to solve the problem.
  • #1
ArcanaNoir
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4

Homework Statement


Let [itex] f(x,y)=xe^{-xy} [/itex] [itex] x \geq 0, y \geq 1 [/itex]
is this a probability density function? If not, find a constant that makes it a pdf.


Homework Equations



To be a pdf, we must have [itex] \int_1^\infty \int_0^\infty \! xe^{-xy} \, \mathrm{d} x \mathrm{d} y=1 [/itex]

The Attempt at a Solution



My problem is, I find the integral to be not convergent. So does my calculator. Do I have the bounds wrong? What's wrong here?

And don't mind the infinity in the integral, I know you're supposed to put the limit as some dummy variable goes to infinity.
 
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  • #2
Never mind, I reversed the order of integration and everything was fine. It didnt occur to me you could do that, new material.
 

1. What is a probability density function?

A probability density function (PDF) is a mathematical function that describes the relative likelihood of a random variable taking on a specific value. It is used to model continuous random variables and is often represented graphically as a curve on a graph.

2. What does it mean when a probability density function integral does not converge?

When a probability density function integral does not converge, it means that the area under the curve is infinite or undefined. This could happen if the function has a very high peak or a long tail, making the total area under the curve too large to be calculated.

3. Why is it important for a probability density function integral to converge?

Convergence of a probability density function integral is important because it ensures that the total probability of all possible outcomes is equal to 1. If the integral does not converge, the probability of certain outcomes may be greater than 1, which is not possible in a valid probability distribution.

4. What are some factors that can cause a probability density function integral to not converge?

Some factors that can cause a probability density function integral to not converge include extreme values of the function, a function that increases too rapidly or has a long tail, or a function that is not defined over the entire range of the random variable.

5. How can a probability density function integral be made to converge?

To make a probability density function integral converge, the function can be modified or restricted in certain ways, such as truncating the tail or rescaling the function. Alternatively, a different type of function or distribution can be used that is known to have a converging integral.

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