MHB Two more PMF/joint PMF questions

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The discussion addresses two questions related to probability mass functions (PMF) and convergence. For question 2, there is confusion about how a certain series converges to one, with one participant suggesting a ratio test that leads to a conclusion of zero instead. In question 3, participants analyze the setup of a PMF where Y is defined as the minimum of 0 and X, questioning the probabilities assigned to various outcomes. They note that since Y cannot exceed 0, certain probabilities, like P(Y>0) and P(Y<a), are zero, while discussing the values for Y when X is uniformly distributed. The conversation emphasizes the need for clarity in the setup and calculations of these probabilities.
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Please refer to the attached images.

For question 2,
How does this converge to one? i tried using a ratio test but thought it converged to zero

For question 3,
I understand how they get a $\frac{1}{1+b-a}$ in the denominator, but not how they set up the rest.
for example if Y = min(0,X)
then Y>0 is impossible because the min(0,X) restricts the value of Y to 0. Thus making $P(Y>0) = 0$
Similarly, $P(Y<a) = 0$ since the bounds are restricted from $a<0<b$.

for $y=0$ this can occur when X takes any value over $[0,b]$. There are $b+1$ such values.

However, for $a<y<0$, aren't there $0-a+1$ such values? Why do they simply have a $1$ in the numerator.
 

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nacho said:
Please refer to the attached images.

For question 2,
How does this converge to one? i tried using a ratio test but thought it converged to zero

Remembering the well known expansion...

$\displaystyle e^{\lambda} = \sum_{n=0}^{\infty} \frac{\lambda^{n}}{n!}\ (1)$

... it is immediate to write...

$\displaystyle e^{- \lambda}\ \sum_{n=0}^{\infty} \frac{\lambda^{n}}{n!}= e^{-\lambda}\ e^{\lambda} = 1\ (2)$

Kind regards

$\chi$ $\sigma$
 
nacho said:
For question 3,
I understand how they get a $\frac{1}{1+b-a}$ in the denominator, but not how they set up the rest.
for example if Y = min(0,X)
then Y>0 is impossible because the min(0,X) restricts the value of Y to 0. Thus making $P(Y>0) = 0$
Similarly, $P(Y<a) = 0$ since the bounds are restricted from $a<0<b$.

for $y=0$ this can occur when X takes any value over $[0,b]$. There are $b+1$ such values.

However, for $a<y<0$, aren't there $0-a+1$ such values? Why do they simply have a $1$ in the numerator.

If X is uniformely distributed in $a \le X \le b$ and is $a < 0$ then... $\displaystyle P \{X = k \} = \frac{1}{1 + b - a}\ \forall k\ \text{with}\ a \le k \le b\ (1)$

If $Y = \text{min}\ (0,X)$ then is $\displaystyle P \{Y = k \} = \frac{1}{1 + b - a}\ \forall k\ \text{with}\ a \le k \le - 1$, $\displaystyle P \{Y = k \} = \frac{1+b}{1 + b - a}\ \text{if}\ k =0$ and 0 for all other k... Kind regards $\chi$ $\sigma$
 
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