Probability density function homework

In summary, the conversation discusses finding a constant c that makes the function f(x,y)=cx2 + e-y a proper probability density function. The speaker suggests setting the integral of f(y) equal to 1 and solving for c, but the other speaker points out that for c=0, the integral cannot converge. The conversation concludes that there is no real c that would make the function a proper probability density function.
  • #1
kingwinner
1,270
0
Find a constant c such that f(x,y)=cx2 + e-y, -1<x<1, y>0, is a proper probability density function.

My idea:
f(y)
1
=∫ f(x,y) dx
-1

So I have found f(y), now I set the following integral equal to 1 in order to solve for c:


∫ f(y) dy = 1
0

Integrating, I get something like (c)(∞)+...=1

If this is the case, how can I solve for c? (can't divide something by infinity) Is this question even possible?

Thanks!
 
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  • #2
Try c=0. It is the only number that could possibly have a finite limit when multiplied by infinity.
 
  • #3
kingwinner said:
1
∫ f(x) dx = 1
-1

Integrating, I get something like (c)(∞)+...=1
How? Can you describe?
 
  • #4
EnumaElish said:
How? Can you describe?

Performing the integartion, I get
f(y)=
1
∫ cx2 + e-y dx
-1
=2c/3 + 2e-y

Setting

∫ f(y)dy=1
0
I get 2c/3 (∞) + 2 =1
 
  • #5
kingwinner said:
=2c/3 + 2e-y
Look at your 2c/3 term, it is a constant wrt y. Any constant, integrated from 0 to infinity, is infinite. It must be zero in order for the integral to converge.
 
  • #6
DaleSpam said:
Look at your 2c/3 term, it is a constant wrt y. Any constant, integrated from 0 to infinity, is infinite. It must be zero in order for the integral to converge.

For 2c/3 (∞) + 2 =1
Put c=0
=> 2=1 (if the first term is zero)
So c=0 doesn't give a proper probability density function...
 
  • #7
kingwinner said:
Find a constant c such that f(x,y)=cx2 + e-y, -1<x<1, y>0, is a proper probability density function.
No such real c exists.
 

1. What is a probability density function (PDF)?

A probability density function (PDF) is a mathematical function that describes the likelihood of a random variable taking on a certain value within a given range. It is represented by a curve on a graph and the area under the curve represents the probability of the random variable falling within that range.

2. How is a PDF different from a probability distribution function (PDF)?

A probability distribution function (PDF) is a general term that refers to any function that describes the probability of a random variable taking on a certain value. A probability density function (PDF) specifically refers to continuous random variables, while a probability mass function (PMF) is used for discrete random variables.

3. How do you calculate the mean and variance of a PDF?

The mean of a PDF is calculated by taking the integral of the product of the random variable and the PDF over its entire range. The variance is calculated by taking the integral of the squared difference between the random variable and the mean over its entire range. Both of these calculations require knowledge of calculus.

4. Can a PDF have a negative value?

No, a probability density function (PDF) cannot have a negative value. This is because a PDF represents the probability of a random variable taking on a certain value, and probabilities cannot be negative.

5. How is a PDF used in real-world applications?

A probability density function (PDF) is used in many real-world applications, such as in finance to model stock prices and in engineering to model failure rates. It is also commonly used in data analysis to describe the distribution of a dataset and make predictions based on that distribution.

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