What is the process for finding the constant A in a probability distribution?

In summary, the probability distribution for finding an object that can move anywhere along an x-axis is given by: A x^2 exp(-x^2/a^2). The Attempt at a Solution integrates from -∞ to ∞ to find A. integrating from -∞ to ∞ would require integrating from -∞ to ∞ x 2 or e^(-x^2/a^2), which is impossible with the limits of ∞ and 0. The formula for finding the average position is ∫xP(x) dx with the limits of +∞ and -∞.
  • #1
chris_avfc
85
0

Homework Statement



Probability distribution for finding an object that can move anywhere along an x-axis is given by

P(x) = A x^2 exp(-x^2/a^2)


The Attempt at a Solution



I need to find A so it correctly represents a probability distribution.
Am I right in thinking I need to integrate it?

∫P(x) dx = 1
With the limits of ∞ and 0?
 
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  • #2
chris_avfc said:

Homework Statement



Probability distribution for finding an object that can move anywhere along an x-axis is given by

P(x) = A x^2 exp(-x^2/a^2)


The Attempt at a Solution



I need to find A so it correctly represents a probability distribution.
Am I right in thinking I need to integrate it?

∫P(x) dx = 1
With the limits of ∞ and 0?

If the object can move anywhere on the x axis, then you need to integrate from -∞ to ∞. Or since the function is even you could use the limits you used but say

∫P(x) dx = 1/2
 
  • #3
kai_sikorski said:
If the object can move anywhere on the x axis, then you need to integrate from -∞ to ∞. Or since the function is even you could use the limits you used but say

∫P(x) dx = 1/2

that makes sense, thank you.

How do I actually integrate the

e^(-x^2/a^2)

Bit though?

Edit:
Actually as I'm doing it as integration by parts I could make that the part you differentiate right?
 
Last edited:
  • #4
chris_avfc said:
that makes sense, thank you.

How do I actually integrate the

e^(-x^2/a^2)

Bit though?

Edit:
Actually as I'm doing it as integration by parts I could make that the part you differentiate right?

Change variables to y = x/a, so you have a density [itex] f_Y(y) = a^3 A y^2 e^{-y^2},[/itex] so you need to deal with the standard integral [itex] \int_{-\infty}^{\infty} e^{-y^2}\, dy. [/itex] You can find this in many reference sources.

RGV
 
  • #6
Ray Vickson said:
Change variables to y = x/a, so you have a density [itex] f_Y(y) = a^3 A y^2 e^{-y^2},[/itex] so you need to deal with the standard integral [itex] \int_{-\infty}^{\infty} e^{-y^2}\, dy. [/itex] You can find this in many reference sources.

RGV


kai_sikorski said:
Are you sure this wasn't discussed in your lecture or text?

http://en.wikipedia.org/wiki/Gaussian_integral

I don't remember ever seeing it in the lectures, but there something similar on the bottom of the question sheet.
I've attached it, although I'm not entirely sure how to use it.

I'm guessing its due to the other constants involved?
 

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  • #7
chris_avfc said:
I don't remember ever seeing it in the lectures, but there something similar on the bottom of the question sheet.
I've attached it, although I'm not entirely sure how to use it.

I'm guessing its due to the other constants involved?

You're joking, right? The formula you need is right there, in plain view in your attached sheet.

RGV
 
  • #8
Ray Vickson said:
You're joking, right? The formula you need is right there, in plain view in your attached sheet.

RGV


Clearly missing something obvious here, but none of them actually match with the one I've been given, which is to be expected as otherwise it would be too simple, but I still don't get it.
 
  • #9
How is the 3rd equation not exactly what you need... you don't even need to do integration by parts or u substitution.
 
  • #10
kai_sikorski said:
How is the 3rd equation not exactly what you need... you don't even need to do integration by parts or u substitution.

Oh god, I was reading the a and the alpha as the same thing, maybe I should just give up the degree now haha.
Right that is so much simpler now.

Before I make another stupid mistake let me check something else with you.
I then need to find the average position of the object, would this be

∫ x P(x) dx with the limits of +∞ and -∞?
 
  • #11
chris_avfc said:
Oh god, I was reading the a and the alpha as the same thing, maybe I should just give up the degree now haha.
Right that is so much simpler now.

Before I make another stupid mistake let me check something else with you.
I then need to find the average position of the object, would this be

∫ x P(x) dx with the limits of +∞ and -∞?

Sorry to be a pain, but could anyone just confirm with me if my question about the average position is right or wrong?
 
  • #12
Yes. I highly suggest you read your course text/notes. This is an absolutely fundamental fact. You can not hope to pass a probability course without knowing this; or for that matter learn something you might apply in your discipline whatever it may be...
 
  • #13
kai_sikorski said:
Yes. I highly suggest you read your course text/notes. This is an absolutely fundamental fact. You can not hope to pass a probability course without knowing this; or for that matter learn something you might apply in your discipline whatever it may be...

Yeah I've got a lot of work to do, I need practice more than anything so I can start to notice the techniques to use and that.
Am I right about how to find the average position though?

Thanks for all the help by the way mate.
 
  • #14
Yes you are
 

1. What is a probability distribution?

A probability distribution is a mathematical function that describes the likelihood of different outcomes occurring in a random experiment or trial. It shows all the possible values that a random variable can take on and the probability associated with each value.

2. What are the types of probability distributions?

There are several types of probability distributions, including discrete distributions (such as the binomial and Poisson distributions) and continuous distributions (such as the normal and exponential distributions). The type of distribution used depends on the type of data and the characteristics of the variable being studied.

3. How is a probability distribution represented?

A probability distribution can be represented in various ways, including a table, a graph, or a mathematical equation. For discrete distributions, a probability mass function (PMF) is used to represent the probabilities of each possible outcome. For continuous distributions, a probability density function (PDF) is used.

4. What is the difference between a probability distribution and a cumulative distribution?

A probability distribution shows the probabilities of individual outcomes, while a cumulative distribution shows the probability of a random variable being less than or equal to a certain value. The cumulative distribution function (CDF) is the sum of all the probabilities up to a certain value, and it can be used to find the probability of a random variable falling within a certain range.

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

Probability distributions are widely used in various fields, such as finance, medicine, and engineering, to model and analyze real-world situations involving uncertainty. They can be used to make predictions, estimate risks, and make informed decisions. For example, the normal distribution is often used to model stock prices, while the Poisson distribution is commonly used in epidemiology to model the spread of diseases.

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