How does the probability density function handle infinity in integrals?

In summary, when dealing with integrals involving infinity, it is important to take the limit as the upper value goes to infinity. Additionally, the limit should be taken as the upper value of the integral, not the lower value.
  • #1
converting1
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http://gyazo.com/02812d5d8f1d07c72153c9f66740e147

I've dealt with integrals with infinity before. When considering the part x >= 1 , do I take the limit as if it's a very large number? i.e. ## \int_0^{\infty} x^{-2.5} \ dx = 2/3 ## ?
 
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  • #2
Yes, take the limit as the upper value goes to infinity.
 
  • #3
converting1 said:
http://gyazo.com/02812d5d8f1d07c72153c9f66740e147

I've dealt with integrals with infinity before. When considering the part x >= 1 , do I take the limit as if it's a very large number? i.e. ## \int_0^{\infty} x^{-2.5} \ dx = 2/3 ## ?

You mean ##\int_1^\infty##. What you have written would diverge.
 
  • #4
ok thank you
 

FAQ: How does the probability density function handle infinity in integrals?

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

A probability density function (PDF) is a mathematical function that describes the probability of a continuous random variable taking on a certain value within a given range. It is used to represent the distribution of a continuous variable and can take on any value between 0 and 1.

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

A PDF is used to represent the probability of a continuous random variable, while a PMF is used for discrete random variables. This means that a PDF can take on any value within a range, while a PMF can only take on specific values.

3. How is the area under a PDF curve related to probability?

The area under a PDF curve within a given range represents the probability of the random variable falling within that range. The total area under the curve is always equal to 1.

4. Can a PDF have negative values?

No, a PDF cannot have negative values. The value of a PDF at a specific point represents the probability density at that point, and having a negative value would not make sense in terms of probability.

5. What is the difference between a PDF and a cumulative distribution function (CDF)?

A PDF gives the probability of a random variable taking on a specific value, while a CDF gives the probability of the random variable being less than or equal to a certain value. The CDF can be obtained by integrating the PDF over a range of values.

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