Two more PMF/joint PMF questions

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SUMMARY

This discussion focuses on the convergence of probability mass functions (PMFs) and joint PMFs, specifically addressing questions related to uniform distributions and the behavior of random variables. The participants analyze the convergence of a series using a ratio test, concluding that it converges to one rather than zero. They also explore the implications of defining a random variable Y as the minimum of 0 and another variable X, detailing the probabilities associated with different ranges of Y. Key formulas discussed include the PMF for uniformly distributed variables and the expansion of the exponential function.

PREREQUISITES
  • Understanding of probability mass functions (PMFs)
  • Familiarity with uniform distributions
  • Knowledge of convergence tests, specifically the ratio test
  • Basic concepts of random variables and their distributions
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  • Study the properties of uniform distributions in detail
  • Learn about convergence tests in probability theory, focusing on the ratio test
  • Explore the derivation and applications of the exponential function expansion
  • Investigate joint PMFs and their applications in statistical analysis
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Mathematicians, statisticians, and students studying probability theory, particularly those interested in the behavior of random variables and the convergence of series in statistical contexts.

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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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