Expected value of complex gaussian

In summary, the expected value of the expression exp(|z+\mu|) is difficult to obtain in a closed form due to the presence of a Q-function and Bessel function. While an approximate closed form may be possible, it cannot be determined without further mathematical analysis.
  • #1
jashua
43
0
What is the expected value of the following expression

[itex]exp(|z+\mu|)[/itex],

where [itex]\mu[/itex] is a real constant and [itex]z=x+jy[/itex] such that [itex]x[/itex] and [itex]y[/itex] are independent Gaussian random variables each with zero mean and [itex]\sigma^2[/itex] variance.

When I try to take the expectation, I couldn't obtain a gaussian integral, so I couldn't take the expectation. So, can we obtain the expected value of the above exponential in a closed form?
 
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  • #2
After a substitution ##y=r\cos(\phi)##, ##x=r \sin(\phi)-\mu##, the one of the two integrals looks possible. I did not check the other integral.
 
  • #3
Thank you for your reply. Then, I have carried out the following integral:

[itex]\frac{1}{2\pi\sigma^2} \int_{0}^{2\pi}\int_{0}^{∞} exp({-\frac{r^2-2r\mu sin(\phi) + 2\sigma^2r + \mu^2}{2\sigma^2}})rdrd\phi[/itex]

The result is

[itex]exp({-\frac{\mu^2}{2\sigma^2}})\frac{1}{\sqrt{2\pi\sigma^2}}\int_0^{2\pi} (\mu sin(\phi) - \sigma^2) exp(\frac{(\mu sin(\phi) - \sigma^2)^2}{2\sigma^2})Q(-\frac{\mu sin(\phi) - \sigma^2}{\sigma})d\phi [/itex]

If I've correctly found this result, even the first integral does not have a closed form due to the Q-function. So, any idea to find an approximate closed form for this integral?
 
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  • #4
How do you get that Q function? A linear substitution simplifies the first integral to ##(r+r_0)e^{-r^2}## (neglecting prefactors), which gives a Gaussian integral and a component with an antiderivative.
 
  • #5
First, let me correct my result that I have given above. The correct version will be as follows:

[itex]exp({-\frac{\mu^2}{2\sigma^2}})(1+\frac{1}{\sqrt{2\pi\sigma^2}}\int_0^{2\pi} (\mu sin(\phi) - \sigma^2) exp(\frac{(\mu sin(\phi) - \sigma^2)^2}{2\sigma^2})Q(-\frac{\mu sin(\phi) - \sigma^2}{\sigma})d\phi) [/itex]The Q-function has the following form:

[itex]Q(x)=\frac{1}{\sqrt{2\pi}}\int_{x}^{∞}exp(-r^2/2)dr[/itex]

So, the term corresponding to [itex]r_0e^{-r^2}[/itex] can be expressed by the Q-function. If we go other way around, I mean if we take integral w.r.t. [itex]\phi[/itex] first, then we get a Bessel function of the first kind. In either case, we don't have a closed form. So, can we get an approximate closed form?
 
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  • #6
I don't see how you get the Q function.
$$\int_0^\infty r_0 e^{-r^2} dr = r_0 \int_0^\infty e^{-r^2} dr = r_0 \frac{\sqrt{\pi}}{2}$$Edit: Oh, that is a result of the linear substitution.
Okay, I see... and I don't know how to avoid that.
 
  • #7
The lower limit does not become 0 if you expand the expression. I mean the linear substitution changes the lower limit.
 

FAQ: Expected value of complex gaussian

1. What is the expected value of complex Gaussian?

The expected value of complex Gaussian is a complex number that represents the average or mean value of a complex Gaussian random variable. It is calculated by taking the sum of all possible outcomes of the random variable multiplied by their respective probabilities.

2. How is the expected value of complex Gaussian different from the expected value of a real Gaussian?

The expected value of complex Gaussian is different from the expected value of a real Gaussian because it takes into account both the real and imaginary components of the complex number, whereas the expected value of a real Gaussian only considers the real component.

3. What is the relationship between the expected value of complex Gaussian and the variance?

The expected value of complex Gaussian and the variance are both measures of central tendency, but they represent different aspects of the data. The expected value represents the average value of the data, while the variance represents the spread or variability of the data around the expected value.

4. How is the expected value of complex Gaussian used in statistical analysis?

The expected value of complex Gaussian is used in statistical analysis to make predictions about the behavior of a complex Gaussian random variable. It can also be used to calculate other important statistical measures such as the standard deviation and covariance.

5. Can the expected value of complex Gaussian be negative?

Yes, the expected value of complex Gaussian can be negative. This is because it is a complex number that can have both a real and imaginary component, and either or both of these components can be negative.

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