Expectation Value Homework: Integrating Gaussian Distribution

In summary, The conversation is about integrating the function x*p(x) where p(x) is the Gaussian distribution. The poster is having trouble with the integration and wonders if there is an analytic solution, but it is pointed out that there is not. They discuss a simple substitution that can be used for a similar integral, but the poster realizes that it cannot be applied to their specific problem. They then discuss the derivative of the function and the poster apologizes for their mistake.
  • #1
cscott
782
1

Homework Statement



Can somebody help me integrate [tex]\int{x\cdot p(x)}[/tex] where [itex]p(x)[/itex] is the Gaussian distribution (from here http://hyperphysics.phy-astr.gsu.edu/hbase/math/gaufcn.html)

The Attempt at a Solution



I can't really get anywhere. It's true that [itex]\int{e^{x^2}}[/itex] has no analytical solution, right?
 
Last edited:
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  • #2
Yes, it is true that you cannot find the indefinite integral
[tex]\int e^{-x^2}dx[/tex]
in terms of elementary functions (though you can find the definite integral for some choices of upper and lower bound).

However, there is a very simple substitution that will give you
[tex]\int x e^{-x^2}dx[/tex]
 
  • #3
Is [tex]\int e^{-x^2} dx [/tex]

not

[tex]\frac{e^{-x^2}}{-2x}[/tex] + K ?
 
  • #4
But I can't use that easy substitution for [tex]\int{x \cdot e^{-(x-x_0)^2} dx[/tex] for some constant [itex]x_0[/itex], can I?
 
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  • #5
No you can't
 
  • #6
rock.freak667 said:
Is [tex]\int e^{-x^2} dx [/tex]

not

[tex]\frac{e^{-x^2}}{-2x}[/tex] + K ?
No, it's not. Why in the world would you think it was?
 
  • #7
HallsofIvy said:
No, it's not. Why in the world would you think it was?

because [tex]\frac{d}{dx}(\frac{e^{-x^2}}{-2x}) = e^{-x^2}[/tex]
 
  • #8
rock.freak667 said:
because [tex]\frac{d}{dx}(\frac{e^{-x^2}}{-2x}) = e^{-x^2}[/tex]

Why would you think that ?
 
  • #9
dextercioby said:
Why would you think that ?

Oh, don't be so cutting! I can see why the poster would think that and so can you. It's clear that the poster forgot about the derivative of the denominator (in case you hadn't worked that out).
 
  • #10
ah oh my...stupid me...sorry...my bad
 

1. What is the purpose of calculating the expectation value for a Gaussian distribution?

The expectation value is a measure of the average or central tendency of a distribution. For a Gaussian distribution, also known as a normal distribution, the expectation value represents the mean or most probable value of the data. It is often used in statistics and probability to make predictions and draw conclusions about a population.

2. How is the expectation value calculated for a Gaussian distribution?

The expectation value for a Gaussian distribution can be calculated using the formula E(x) = µ, where µ is the mean or average of the data. This means that the expectation value for a Gaussian distribution is equal to the mean of the data set.

3. What is the relationship between the expectation value and the standard deviation of a Gaussian distribution?

The standard deviation is a measure of the spread or variability of a distribution, while the expectation value represents the center or average. For a Gaussian distribution, the standard deviation is equal to the square root of the variance, which is the average of the squared differences from the mean. This means that the standard deviation gives an indication of how much the data deviates from the expectation value.

4. Can the expectation value be used to predict the exact value of a data point in a Gaussian distribution?

No, the expectation value only represents the most probable or average value of a distribution. It cannot be used to predict the exact value of a data point, as there is always a degree of uncertainty in the data. However, it can be used to make probabilistic predictions about the likelihood of a data point falling within a certain range.

5. How is the expectation value used in real-world applications?

The expectation value is used in a variety of fields, including physics, finance, and engineering. In physics, it is used to calculate the expected outcome of experiments or to make predictions about the behavior of systems. In finance, it is used to analyze stock market trends and make investment decisions. In engineering, it is used to optimize processes and predict the performance of systems.

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