Finding the PMF of Y with Known X

  • MHB
  • Thread starter Dustinsfl
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In summary, the problem is asking for the PMF of Y when X is known, where Y is defined as the sine of pi divided by twice X, and X has a uniform distribution over the values 0 to 4. It is suggested to look for theorems about characteristic functions of transformed random variables to solve this problem. However, there seems to be some ambiguity in the notation for the probability distribution of X.
  • #1
Dustinsfl
2,281
5
How do I find the PMF of Y when I know X?
\[
Y = \sin\Big(\frac{\pi}{2X}\Big)
\]
and
\[
p_X[k] = \frac{1}{5}
\]
for \(k = 0,1,\ldots, 4\).
 
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  • #2
Maybe there're several ways to solve this problem. It's clear that $Y$ is a transformation of $X$. So have you seen any theorems about characteristic funtions of transformated random variables?

EDIT:
I now see that this problem is already solved.
 
  • #3
Siron said:
...EDIT:
I now see that this problem is already solved.

Well, the [SOLVED] prefix has been added, but I see no solution...
 
  • #4
dwsmith said:
How do I find the PMF of Y when I know X?
\[
Y = \sin\Big(\frac{\pi}{2X}\Big)
\]
and
\[
p_X[k] = \frac{1}{5}
\]
for \(k = 0,1,\ldots, 4\).

There is a point not clear: writing $\displaystyle p_X[k] = \frac{1}{5}, k= 0,1,2,3,4$ means writing $\displaystyle P\{X=k \} = \frac{1}{5}, k= 0,1,2,3,4$?...

Kind regards

$\chi$ $\sigma$
 
  • #5
chisigma said:
There is a point not clear: writing $\displaystyle p_X[k] = \frac{1}{5}, k= 0,1,2,3,4$ means writing $\displaystyle P\{X=k \} = \frac{1}{5}, k= 0,1,2,3,4$?...

Kind regards

$\chi$ $\sigma$

The reason of my question is that for X=0 defining $\displaystyle Y = \sin \frac{\pi}{2\ X}$ is a little difficult task (Dull)...

Kind regards

$\chi$ $\sigma$
 

FAQ: Finding the PMF of Y with Known X

What is the meaning of PMF?

PMF stands for Probability Mass Function. It is a statistical concept that describes the probability of discrete values occurring in a set of data.

How do you calculate the PMF of Y when we know X?

To calculate the PMF of Y when we know X, we use the formula P(Y=y|X=x) = P(Y=y and X=x) / P(X=x). This means we take the probability of Y and X occurring together and divide it by the probability of only X occurring.

Can the PMF of Y when we know X be used for continuous data?

No, the PMF is only applicable for discrete data. For continuous data, we use the Probability Density Function (PDF) instead.

What is the difference between PMF and CDF?

The PMF represents the probability of a specific value occurring in a set of data, while the Cumulative Distribution Function (CDF) represents the probability of a value less than or equal to a certain point. The PMF is discrete while the CDF can be used for both discrete and continuous data.

How is PMF used in practical applications?

PMF is frequently used in data analysis and modeling to understand the probability of specific outcomes occurring. It can also be used to compare different groups or populations and identify patterns or trends in the data.

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