Statistical moments and multipole moments

In summary, the conversation discusses deriving moments in statistics using a generating function, and whether a similar method can be used for deriving the multipole moments in electrodynamics. The connection between the expansion of \frac {1}{\left|x\right|} and the multipole moments is also mentioned, with a suggestion to further research the topic for more information.
  • #1
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Hello,

in statistics, one can derive the moments of a distribution by using a generating function
[tex]<x^n> = \int dx x^n f(x) = \left( \frac {d}{dt} \int dx \exp(tx) f(x) \right)_{t=0} = \left( \frac d {dt} M(t) \right)_{t=0}[/tex]

Is there a similar method to derive the multipole moments in electrodynamics, e.g. is there a generating function? I know that the multipole moments are derived from the expansion of
[tex]\frac {1}{\left|x\right|}[/tex]
but I don't seem to get the connection to a generating function.
 
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  • #2
Hi there,

I'm not sure if there is a similar method to deriving the multipole moments in electrodynamics using a generating function as with statistics. I think the multipole moments are derived from the expansion of \frac {1}{\left|x\right|} because this is a measure of the electric field at a given point, which is related to the multipole moments. Have you tried researching this further? Maybe someone else on the forum may have more information on this topic that can help provide more insight.
 
  • #3


Hello,

Thank you for your question. In electrodynamics, the multipole moments are derived from the expansion of the electric potential, which is given by the Coulomb potential:

V(\mathbf{r}) = \frac{1}{4\pi\epsilon_0} \int \frac{\rho(\mathbf{r'})}{|\mathbf{r}-\mathbf{r'}|}d^3\mathbf{r'}

The multipole moments are coefficients in the expansion of this potential in terms of spherical harmonics:

V(\mathbf{r}) = \frac{1}{4\pi\epsilon_0} \sum_{l=0}^\infty \sum_{m=-l}^l \frac{4\pi}{2l+1}q_{lm} \frac{Y_{lm}(\theta,\phi)}{r^{l+1}}

where q_{lm} are the multipole moments and Y_{lm} are the spherical harmonics.

To answer your question, there is no direct connection to a generating function in the derivation of multipole moments. However, one can use the method of moments to determine the multipole moments from the charge distribution. This involves taking the moments of the charge distribution and equating them to the multipole moments in the expansion of the potential. So in a sense, the moments of the charge distribution act as a generating function for the multipole moments.

I hope this helps clarify the connection between statistical moments and multipole moments in electrodynamics. Let me know if you have any further questions.
 

1. What are statistical moments and multipole moments?

Statistical moments and multipole moments are mathematical tools used to describe the distribution of data or charges in a physical system. Statistical moments are used in statistics to quantify the shape and variability of a distribution, while multipole moments are used in physics to describe the distribution of electric charge or mass in a system.

2. How are statistical moments and multipole moments calculated?

Statistical moments are calculated by taking the nth power of each data point, multiplying it by its respective frequency or probability, and then summing all of the values. Multipole moments are calculated by integrating the product of the charge or mass distribution and the distance from a reference point, raised to the nth power. The integration is performed over the entire system.

3. What is the significance of statistical moments and multipole moments in data analysis?

Statistical moments and multipole moments provide valuable insights into the distribution of data and charges in a system. They can help identify patterns, trends, and anomalies in the data, and can also be used to compare and contrast different distributions or systems.

4. Can statistical moments and multipole moments be used in any type of data or system?

Yes, statistical moments and multipole moments can be applied to a wide range of data and systems in various fields such as physics, engineering, biology, and finance. However, the specific moments and their interpretations may vary depending on the type of data or system being analyzed.

5. Are there any limitations or assumptions associated with statistical moments and multipole moments?

Like any mathematical tool, statistical moments and multipole moments have limitations and assumptions. They are most effective when the data or system being analyzed is well-behaved and follows a known distribution. Additionally, some moments may be sensitive to outliers or biased by the choice of reference point in the case of multipole moments.

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