Convolution of characteristic function

In summary: integrations, or can we just do:\begin{align}\chi_{[-a,a]} * \chi_{[-a,a]} * \chi_{[-a,a]}\end{align}
  • #1
alena_S
32
0
Member warned about posting with no effort shown

Homework Statement


I am trying to figure out following problem.

Let A ⊂ R. Then we can define the characteristic function:
\begin{align}
\chi_A : R → \{0, 1\}, x = \begin{cases}
1 & \text{if } x \in A \\
0 & \text{else }
\end{cases}
\end{align}

Let a be bigger than 0. I am trying to find a following convolution:
\begin{align}
\chi_{[-a,a]} * \chi_{[-a,a]} * \chi_{[-a,a]}
\end{align}

Homework Equations


Convolution is given as
\begin{align}
f*g = \int f(x-y) g(y) dy
\end{align}

The Attempt at a Solution


I have started to do convolution of 1st 2 terms, my results are as follows(not sure about correctness)[/B]

\begin{align}
\phi * \phi (x) = \begin{cases}
0 & x \leq -a \\
x & \text{ if } -a \leq x \leq a\\
2a - x & \text{ if } a \leq x \leq 2a\\
0 & x \geq 2a
\end{cases}

\end{align}
but I am being stuck what should follow.
 
Last edited:
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  • #2
I don't see a problem statement yet. What do you want to convolve ##\chi_A## with? Do you know more about ##A##? Is the domain of the convolution integral the entire real line?
 
  • #3
sorry, there was a typo in post. I hope, its correct now
 
  • #4
alena_S said:
sorry, there was a typo in post. I hope, its correct now

No, it is NOT correct. The notation ##\chi_A[-a,a]## makes no sense. If you mean that ##A = [-a,a]##, then you must write ##\chi_{[-a,a]}##. If you mean something else, tell us what you mean.
 
  • #5
Ray Vickson said:
No, it is NOT correct. The notation ##\chi_A[-a,a]## makes no sense. If you mean that ##A = [-a,a]##, then you must write ##\chi_{[-a,a]}##. If you mean something else, tell us what you mean.
Indeed, I meant ##\chi_{[-a,a]}##.
 
  • #6
alena_S said:
Indeed, I meant ##\chi_{[-a,a]}##.

OK, so one way is to use Laplace transforms. Usually you see Laplace transforms defined for functions on ##[0, \infty)## but they CAN be defined for functions on ##[-M,\infty)## in the same way: for ##f:[-M,\infty) \to \mathbb{R}##, the Laplace transform is
[tex] {\cal L}_f (s) = \int_{-M}^{\infty} f(x) e^{-st} \, dt, [/tex]
and has the usual properties. In particular, the transform of a convolution is the product of the transforms. Then you can invert the result to get your function in terms of ##x##.

Alternatively, you can apply the straight definition of convolution and compute the results directly by integration, doing first the function ##f_2 = \chi_{[-a,a]} * \chi_{[-a,a]}##, then doing another integration to get ##f_3 = \chi_{[-a,a]} * \chi_{[-a,a]} * \chi_{[-a,a]} ## as ##f_3 = f_2 * \chi_{[-a,a]} ##. Keeping track of integration limits and spitting the calculation up into cases is tricky but necessary.

By the way: you expression for ##f_2## in your eq. (4) is not correct: the lower and upper ##x##-limits are wrong. Basically, your functions ##\chi_{[-a,a]}## are (up to a scale factor) the probability density functions of random variables uniformly distributed between ##-a## and ##+a##. The two-fold convolution is the density function of a sum of two independent uniformly-distributed random variables, and so will be positive on values of ##x## ranging from ##-2a## to ##+2a##. For the three-fold convolution that range will be from ##-3a## to ##+3a##.
 
  • #7
Ray Vickson said:
OK, so one way is to use Laplace transforms. Usually you see Laplace transforms defined for functions on ##[0, \infty)## but they CAN be defined for functions on ##[-M,\infty)## in the same way: for ##f:[-M,\infty) \to \mathbb{R}##, the Laplace transform is
[tex] {\cal L}_f (s) = \int_{-M}^{\infty} f(x) e^{-st} \, dt, [/tex]
and has the usual properties. In particular, the transform of a convolution is the product of the transforms. Then you can invert the result to get your function in terms of ##x##.

Alternatively, you can apply the straight definition of convolution and compute the results directly by integration, doing first the function ##f_2 = \chi_{[-a,a]} * \chi_{[-a,a]}##, then doing another integration to get ##f_3 = \chi_{[-a,a]} * \chi_{[-a,a]} * \chi_{[-a,a]} ## as ##f_3 = f_2 * \chi_{[-a,a]} ##. Keeping track of integration limits and spitting the calculation up into cases is tricky but necessary.
Is my attempt of f2 correct (eq 4 on beginning)? just to know, if should continue with such a result, or rather try it again?
 
  • #8
alena_S said:
Is my attempt of f2 correct (eq 4 on beginning)? just to know, if should continue with such a result, or rather try it again?

Read my edited message # 6, as it deals exactly with that issue. I posted the edited version before your new message appeared on my screen; I often encounter such PF delays.
 
  • #9
Ray Vickson said:
Read my edited message # 6, as it deals exactly with that issue. I posted the edited version before your new message appeared on my screen; I often encounter such PF delays.

So, should it be as:
for f2:

\begin{align}
\phi * \phi (x) = \begin{cases}
0 & x \leq -2a \\
x & \text{ if } -2a \leq x \leq a\\
2a - x & \text{ if } a \leq x \leq 2a\\
0 & x \geq 2a
\end{cases}
\end{align}

And for f3, do we need 7 different intervals, or did I get it wrong?
 
  • #10
alena_S said:
So, should it be as:
for f2:

\begin{align}
\phi * \phi (x) = \begin{cases}
0 & x \leq -2a \\
x & \text{ if } -2a \leq x \leq a\\
2a - x & \text{ if } a \leq x \leq 2a\\
0 & x \geq 2a
\end{cases}
\end{align}

And for f3, do we need 7 different intervals, or did I get it wrong?

Still wrong: the graph of ##y = f_2(x)## should be an isosceles triangle.

Basically, you are taking the convolution of even functions, so ought to get back an even function. After all: if ##f_2(x) = \int_{-\infty}^{\infty} f(y) f(x-y) \, dy##, then without actually doing the integral you can see that ##f \;\text{even} \longrightarrow f_2 \; \text{even}##, just by changing variables and doing some manipulations. It becomes intuitively obvious if you use the "probabilistic" interpretation of the problem.
 
Last edited:
  • #11
Does this look more correct?

\begin{align}
\phi * \phi (x) = \begin{cases}
0 & x > 2a \ \ \text{or} \ \ x < -2a \\
x + 2a & \text{ if } x \leq [-2a, 0]\\
2a - x & \text{ if } x \in [0,2a]\\
\end{cases}
\end{align}
 
  • #12
alena_S said:
Does this look more correct?

\begin{align}
\phi * \phi (x) = \begin{cases}
0 & x > 2a \ \ \text{or} \ \ x < -2a \\
x + 2a & \text{ if } x \leq [-2a, 0]\\
2a - x & \text{ if } x \in [0,2a]\\
\end{cases}
\end{align}

Not "more" correct; just plain correct.

I hope you got the result by actual calculation, not by "guessing", because you will not easily guess the formula for ##f_3##.
 
  • #13
Ray Vickson said:
Not "more" correct; just plain correct.

I hope you got the result by actual calculation, not by "guessing", because you will not easily guess the formula for ##f_3##.

After some steps, I have obtained following for f3:
$$\chi \ast \chi \ast \chi(x) = \begin{cases} 0, & x \notin [-2a,2a], \\ \int 1_{[-a,2a+x]}(y) \cdot (x+2a) \, dy, & x \in [-2a,-a], \\ \int 1_{[x,a]}(y) \cdot (x+2a) + 1_{[-a,x]}(y) \cdot (2a-x) \, dy, & x \in [-a,a] \\ \int 1_{[-2a+x,a]}(y) \cdot (2a-x) \,dy, & x \in [a,2a]. \end{cases}$$,
which is after evaluation of integrals results into

$$\chi \ast \chi \ast \chi(x) = \begin{cases}
0, & x \notin [-2a,2a], \\
(x+2a) & x \in [-2a,-a], \\
4a & x \in [-a,a] \\
(2a-x) & x \in [a,2a].
\end{cases}$$
 
Last edited:
  • #14
Ray Vickson said:
Not "more" correct; just plain correct.

I hope you got the result by actual calculation, not by "guessing", because you will not easily guess the formula for ##f_3##.
Is this what I am supposed to get?
 
  • #15
alena_S said:
Is this what I am supposed to get?

No, nothing like it. Go back to square 1.
 
  • #16
Ray Vickson said:
No, nothing like it. Go back to square 1.

Evaluation of integrals is wrong? or all result?
 
  • #17
alena_S said:
Evaluation of integrals is wrong? or all result?

Result is wrong, so integrals must be evaluated or written incorrectly as well. These are my last words on the topic.
 
  • #18
alena_S said:
Evaluation of integrals is wrong? or all result?
It would help immensely if, rather than simply posting your final result, you showed your work and explained your reasoning. We can't see your paper, so we don't know what you did. And trying to guess what you did gets old really fast. Even if you get the right result, you could still be making mistakes.
 

Related to Convolution of characteristic function

1. What is the definition of convolution of characteristic function?

The convolution of characteristic function is a mathematical operation that combines two probability distributions to produce a third distribution. It is defined as the integral of the product of the characteristic functions of the two distributions.

2. What is the purpose of convolution of characteristic function?

The purpose of convolution of characteristic function is to find the probability distribution of a sum of independent random variables. It is widely used in probability theory and statistics to analyze the behavior of complex systems.

3. How is convolution of characteristic function related to the central limit theorem?

The central limit theorem states that the sum of a large number of independent random variables will tend towards a normal distribution. The convolution of characteristic function provides a way to calculate the probability distribution of this sum, making it an essential tool in understanding the central limit theorem.

4. Can convolution of characteristic function be used for non-independent random variables?

Yes, convolution of characteristic function can be applied to both independent and non-independent random variables. However, for non-independent variables, the resulting distribution may not always be well-defined.

5. Are there any practical applications of convolution of characteristic function?

Yes, convolution of characteristic function has many practical applications in fields such as signal processing, image processing, and machine learning. It is used to analyze complex data and make predictions about future events based on probability distributions.

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