Recovering PMF from characteristic equation

In summary, the conversation discusses the integration of the characteristic equation to recover the PMF, but the resulting answer is zero, indicating an error. The conversation also addresses the issue of defining the PMF for all integers, including -1 and 1, which would result in a strange definition since the sine function is zero at these values. The conversation concludes by questioning what went wrong in the process.
  • #1
Dustinsfl
2,281
5
I am integrating the characteristic equation in order to recover the PMF, but I am going to get the answer to be zero so something went wrong.
\begin{align*}
p_X[k]
&= \frac{1}{2\pi}\int_{-\pi}^{\pi}\phi_X(\omega)e^{-i\omega k}d\omega\\
&= \frac{1}{2\pi}\int_{-\pi}^{\pi}\cos(\omega)e^{-i\omega k}d\omega\\
&= \frac{i}{2\pi k}\cos(\omega)e^{-i\omega k}\bigg|_{-\pi}^{\pi} +
\frac{i}{2\pi k}\int_{-\pi}^{\pi}\sin(\omega)e^{-i\omega k}d\omega\\
&= \frac{i}{2\pi k}(e^{i\pi k} - e^{-i\pi k}) + \frac{i}{2\pi k}\Bigg[
\frac{i}{k}\sin(\omega)e^{-i\omega k}\bigg|_{-\pi}^{\pi} -
\frac{i}{k}\int_{-\pi}^{\pi}\cos(\omega)e^{-i\omega k}d\omega\Bigg]\\
&= \frac{-1}{\pi k}\bigg(\frac{e^{i\pi k} - e^{-i\pi k}}{2i}\bigg) +
\frac{1}{2\pi k^2}\int_{-\pi}^{\pi}\cos(\omega)e^{-i\omega k}d\omega\\
\bigg(\frac{k^2 - 1}{2\pi k^2}\bigg)
\int_{-\pi}^{\pi}\cos(\omega)e^{-i\omega k}d\omega
&= \frac{-\sin(\pi k)}{\pi k}\\
\int_{-\pi}^{\pi}\cos(\omega)e^{-i\omega k}d\omega
&= \frac{-2k\sin(\pi k)}{k^2 - 1}
\end{align*}
Since k is an integer, the RHS is zero.
Additionally, the \(p_X[k]\) is supposed to be defined for all integers, but if I say \(p_X[k] = \frac{-2k\sin(\pi k)}{k^2 - 1}\), k cannot be -1 or 1. This definition of the PMF would be weird anyways since \(\sin(\pi k) = 0\) anyways.

What went wrong?
 
Last edited:
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  • #2
You can compute the limits $k \to 1$ and $k \to -1$.
 

FAQ: Recovering PMF from characteristic equation

What is PMF?

PMF stands for Probability Mass Function. It is a statistical concept that describes the probability of a discrete random variable taking on a specific value.

What is the characteristic equation?

The characteristic equation is an algebraic equation that is used to find the roots or solutions of a polynomial function. In the context of probability, it is used to find the values of the variable for which the PMF equals a certain probability.

How is PMF recovered from the characteristic equation?

To recover the PMF from the characteristic equation, we need to solve the equation for the variable of interest. This will give us the specific values of the variable for which the PMF equals a certain probability. These values can then be used to construct the PMF function.

Can the characteristic equation be used for any type of random variable?

Yes, the characteristic equation can be used for any type of discrete random variable, as long as it follows a known distribution. Some examples of commonly used distributions include the binomial, Poisson, and geometric distributions.

Are there any limitations to using the characteristic equation to recover PMF?

One limitation of using the characteristic equation is that it can only be used for discrete random variables. It cannot be applied to continuous random variables. Additionally, the characteristic equation may be difficult to solve for some distributions, and in those cases, numerical methods may be needed.

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