Proving function f(x) is a PDf given integral relationships

In summary, the problem is to prove that the given integral is equal to 1 and thus a probability density function, given that c is a maximum and that the integral of f(x) from 0 to pi^2 is equal to ln(pi^2 - e). The approach may involve expressing the integral in terms of a variable and solving for c, but further clarification on the relationship involving c is needed.
  • #1
Saracen Rue
150
10

Homework Statement


Prove that ##\int _0^a\left(\frac{f\left((sin(ln(c))x\right)+\sqrt{\cos \left(e-\pi ^2\right)}}{\ln \left(\pi ^2-e\right)+\pi ^2\sqrt{\cos \left(e-\pi ^2\right)}}\right)dx## is a probability density function (when ##a=\frac{1}{\pi ^2}##) given that ##\int _0^{\pi ^2}f\left(x\right)dx=\ln \left(\pi ^2-e\right)## and that ##c## is a maximum.

Homework Equations


##\int _a^bf\left(x\right)dx=F\left(b\right)-F\left(a\right)##

The Attempt at a Solution


I'm honestly completely stuck with this question. I know that ##\int _0^a\left(\frac{f\left((sin(ln(c))x\right)+\sqrt{\cos \left(e-\pi ^2\right)}}{\ln \left(\pi ^2-e\right)+\pi ^2\sqrt{\cos \left(e-\pi ^2\right)}}\right)dx## can be expressed as ##\frac{1}{\ln \left(\pi ^2-e\right)+\pi ^2\sqrt{\cos \left(e-\pi ^2\right)}}\int _0^a\left(f\left((sin(ln(c))x\right)+\sqrt{\cos \left(e-\pi ^2\right)}\right)dx##, but I am unsure of how this helps me to prove that the integral is equal to 1 (thus proving it is a PDf). By specifying that ##c## is a maximum, the question insinuates that ##c## is a variable that can be expressed in terms of another variable which in turn can be derived and solved, however I am unsure of how to form such a relationship.
 
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  • #2
c is a maximum of what?

There are mismatched brackets related to f.
It looks like the problem depends on c, which is odd. What exactly do you know about c?
 

FAQ: Proving function f(x) is a PDf given integral relationships

What is a PDF (Probability Density Function)?

A PDF is a mathematical function that describes the probability distribution of a continuous random variable. This means that it shows all the possible values that a random variable can take and the likelihood of each value occurring.

How do you prove that a function f(x) is a PDF?

To prove that a function f(x) is a PDF, you need to show that it satisfies two conditions: it must be non-negative for all values of x and its integral over the entire range of x must equal to 1.

Can a function have multiple PDFs?

No, a function can only have one PDF. This is because a PDF is a specific representation of the probability distribution of a random variable, and a random variable can only have one probability distribution.

Can a PDF be used to determine the probability of a specific value?

No, a PDF cannot be used to determine the probability of a specific value. Instead, it gives the probability of a range of values. The probability of a specific value can be found by taking the limit of the PDF as the range approaches that value.

How is a PDF related to the cumulative distribution function (CDF)?

The CDF is the integral of the PDF, which means that it represents the cumulative probability of a random variable taking on values less than or equal to a certain value. In other words, the CDF is the area under the PDF curve, and the PDF is the derivative of the CDF.

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