Solving integral - gaussian distribution of cos

In summary: The Attempt at a Solutioni can change the integral to 0:infinity, since sigma << L_{av}. Then i have to look up some integral solution, probably:∫(0:infinity) cos(bx)*e^-ax^2 dxI assume i have to do some trick like 1/L approximately L/L_{av^2}, but how can i justify that?Can you use complex numbers? If so, you can rewrite your cosine in term of complex exponentials which will make things quite easy.
  • #1
poul
17
0

Homework Statement



I have to prove:

∫(-infinity:infinity) cos(pi*v/2L)*e^-((L-L_av)^2/sqrt(2pi)*sigma^2) dL proportional to
cos(pi*v/2L_av)*e^-(t/tau)^2

tau is some constant, and sigma << L_av.

The Attempt at a Solution



i can change the integral to 0:infinity, since sigma << L_av. Then i have to look up some integral solution, probably:

∫(0:infinity) cos(bx)*e^-ax^2 dx

I assume i have to do some trick like 1/L approximately L/L_av^2 - but how can i justify that?
 
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  • #2
Can you use complex numbers? If so, you can rewrite your cosine in term of complex exponentials which will make things quite easy.
 
  • #3
poul said:

Homework Statement



I have to prove:

∫(-infinity:infinity) cos(pi*v/2L)*e^-((L-L_av)^2/sqrt(2pi)*sigma^2) dL proportional to
cos(pi*v/2L_av)*e^-(t/tau)^2

tau is some constant, and sigma << L_av.

The Attempt at a Solution



i can change the integral to 0:infinity, since sigma << L_av. Then i have to look up some integral solution, probably:

∫(0:infinity) cos(bx)*e^-ax^2 dx

I assume i have to do some trick like 1/L approximately L/L_av^2 - but how can i justify that?

It is very unclear what your original problem is, because you do not use brackets. You have something written as L_av. Does this mean Lav or does it mean Lav? If you mean the first, write L_{av} or L_(av), and if you mean the second, either write a*L_v or (L_v)a. Better still, use the "X2" button in the menu above the input panel. Anyway, setting L_av = c, some constant, it is still not clear whether you mean
[tex] \cos \left( \frac{\pi v}{2L} \right) \text{ or } \cos \left( \frac{\pi v}{2} L \right) [/tex] in the integrand. If you mean the first, write cos(pi*v/(2L)), but if you mean the latter, write cos((pi*v/2)L).

Similarly, when you write e^-ax^2, you are literally writing ##e^{-a} x^2## if we read it using standard rules and conventions. I guess you mean ##e^{-ax^2},## which is e^{-ax^2} or e^(-ax^2) in plain text, or better still, e-ax^2 or e-ax2, using the "X2" button in the menu at the top of the input panel.

RGV
 
  • #4
Okay, I have to prove:

∫(-infinity:infinity) cos(pi*v/(2L))*e-((L-L_{av})^2/(2*sigma^2)) dL proportional to
cos(pi*v/(2L_{av}))*e-(t/tau)^2

sigma << L_{av} - both positive
 

Related to Solving integral - gaussian distribution of cos

1. What is the integral of a Gaussian distribution of cos?

The integral of a Gaussian distribution of cos is not a well-defined mathematical concept. The integral of a function can only be calculated over a specific interval, and a Gaussian distribution is a continuous function that extends to infinity in both directions. Therefore, it is not possible to calculate the exact integral of a Gaussian distribution of cos.

2. How do you solve an integral of a Gaussian distribution of cos?

As mentioned above, the integral of a Gaussian distribution of cos cannot be solved exactly. However, it is possible to approximate the integral using numerical methods such as the trapezoidal rule or Simpson's rule. These methods involve dividing the interval into smaller subintervals and calculating the area under the curve for each subinterval.

3. What is the significance of a Gaussian distribution of cos in statistics?

A Gaussian distribution of cos is used in statistics to model data that is normally distributed. This type of distribution is commonly seen in many natural phenomena, and it allows for the calculation of probabilities and confidence intervals. It is also a key component in the Central Limit Theorem, which states that the sample means of independent random variables will be normally distributed regardless of the underlying distribution.

4. Can the integral of a Gaussian distribution of cos be solved analytically?

No, the integral of a Gaussian distribution of cos cannot be solved analytically. This means that there is no closed-form solution that can be expressed in terms of elementary functions. However, the numerical methods mentioned above can be used to approximate the integral to a desired level of accuracy.

5. How does the standard deviation affect the integral of a Gaussian distribution of cos?

The standard deviation, which measures the spread of the data, plays a crucial role in the shape of a Gaussian distribution of cos. As the standard deviation increases, the curve becomes flatter and wider, resulting in a larger area under the curve. Therefore, a larger standard deviation will result in a larger integral of a Gaussian distribution of cos.

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