Approximating a sum of exponentials

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In summary, the equation given is an asymptotic expression that approximates the sum of a probability distribution, where \theta is a small angle. The expression is derived using the Cumulant expansion method, which involves expanding the exponential factor to second order and calculating the averages. This result holds exactly for a Gaussian distribution of \theta.
  • #1
mooshasta
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I came across the following statement:

[tex]\sum_n p(n)e^{-in\theta} \approx exp[-i\theta \langle n\rangle - \theta^2 \langle ( \delta n)^2 \rangle / 2][/tex]

where [itex]\theta[/itex] is small, [itex]\sum_n p(n) = 1[/itex], [itex]\langle n \rangle = \sum_n p(n)n[/itex], and [itex]\langle ( \delta n)^2 \rangle = \sum_n p(n)(n-\langle n \rangle)^2[/itex].

I am pretty stumped trying to figure out how this asymptotic expression is derived. I tried writing out the exponents as sums to no avail. I can see that [itex](-i\theta)^2 = -\theta^2[/itex] but I am pretty confused regarding the presence of [itex]\langle n \rangle[/itex] and [itex]\langle (\delta n)^2 \rangle[/itex] in the exponential. Any suggestions are greatly appreciated!
 
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  • #2
I did not spend much time on it but if "exp" means the exponential, then this looks like a small angle approximation.
 
  • #3
Look up "Cumulant expansion". Alas, wikipedia has no page for this, but it is the method one would use to arrive at this approximation (and get better approximations). If you expand the exponential factor in the sum to second order and do the averages, the result looks just like the series for the right hand side of the equation to first order.

A related result that holds exactly: If [itex]\theta[/itex] is Gaussian distributed, then

[tex]\langle \exp(i\theta) \rangle = \exp(-\langle \theta^2 \rangle)[/tex]
 

FAQ: Approximating a sum of exponentials

What is the purpose of approximating a sum of exponentials?

The purpose of approximating a sum of exponentials is to simplify complex mathematical expressions involving exponential functions. This allows for easier analysis and interpretation of the data or phenomenon being studied.

How is a sum of exponentials approximated?

A sum of exponentials is typically approximated using a technique called the method of least squares. This involves finding the best-fit curve that minimizes the sum of the squared differences between the actual data points and the predicted values from the exponential function.

What are the advantages of using a sum of exponentials to model data?

One advantage of using a sum of exponentials is that it can accurately model a wide range of data, including both linear and nonlinear relationships. Additionally, it allows for the identification of multiple exponential components within a dataset, providing valuable insights into the underlying patterns and trends.

Are there any limitations to using a sum of exponentials?

While a sum of exponentials can be a useful tool for data analysis, it is not suitable for all types of data. For example, if the data follows a different mathematical function, such as a power law or logarithmic relationship, using a sum of exponentials may not provide an accurate approximation.

How can I determine the accuracy of a sum of exponentials approximation?

The accuracy of a sum of exponentials approximation can be evaluated by comparing the predicted values to the actual data points. This can be done visually by plotting the data and the approximated curve, or quantitatively by calculating the root mean square error or correlation coefficient between the two sets of values.

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