Marginal Probability Mass Functions

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SUMMARY

The discussion focuses on determining the marginal probability mass functions (PMFs) for discrete random variables X and Y, defined by the joint PMF pXY(x,y) = 2^(-x-y). The marginal PMFs are calculated as pX(x) = ∑ p(x,y) and pY(y) = ∑ p(x,y). The independence of X and Y is assessed using the condition p(x,y) = p(x)p(y). The expected values E[X], E[Y], and E[XY] are also derived, with E[XY] being calculated under the assumption of independence.

PREREQUISITES
  • Understanding of joint probability mass functions (JPMFs)
  • Knowledge of marginal probability mass functions (PMFs)
  • Familiarity with the concept of independence in probability
  • Ability to compute expected values for discrete random variables
NEXT STEPS
  • Learn how to derive marginal PMFs from joint PMFs in detail
  • Study the properties of independent random variables in probability theory
  • Explore the computation of expected values for functions of random variables
  • Investigate common pitfalls in notation and representation of probability functions
USEFUL FOR

Students studying probability theory, statisticians analyzing discrete random variables, and educators teaching concepts related to joint and marginal distributions.

twoski
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Homework Statement



Discrete random variables X and Y , whose values are positive integers, have the joint
probability mass function pXY(x,y) = 2-x-y. Determine the marginal probability mass
functions pX(x) and pY(y). Are X and Y independent? Determine E[X], E[Y], and E[XY].

Homework Equations



Independence is determined by whether p(x,y) = p(x)p(y) for all x and y.

The Attempt at a Solution



My notes don't have much on the topic of determining marginal PMF's using a JPMF... I was hoping someone could point me in the right direction.
 
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twoski said:

Homework Statement



Discrete random variables X and Y , whose values are positive integers, have the joint
probability mass function pXY(x,y) = 2-x-y. Determine the marginal probability mass
functions pX(x) and pY(y). Are X and Y independent? Determine E[X], E[Y], and E[XY].

Homework Equations



Independence is determined by whether p(x,y) = p(x)p(y) for all x and y.

The Attempt at a Solution



My notes don't have much on the topic of determining marginal PMF's using a JPMF... I was hoping someone could point me in the right direction.

The marginal pmf of X is
p_X(x) = P\{ X = x, Y \leq \infty\} = \sum_{\text{all }y} p(x,y).
BTW: it is bad form to use the same symbol p to stand for three different things in the same problem. Instead, use subscripts, like this (##p_X(x), p_Y(y)##) or different letters, like this: ##g(x)## and ##h(y)##.
 
Ray Vickson said:
The marginal pmf of X is
p_X(x) = P\{ X = x, Y \leq \infty\} = \sum_{\text{all }y} p(x,y).
BTW: it is bad form to use the same symbol p to stand for three different things in the same problem. Instead, use subscripts, like this (##p_X(x), p_Y(y)##) or different letters, like this: ##g(x)## and ##h(y)##.

So if that's the marginal PMF of x, then for y...

p_Y(y) = P\{ X \leq \infty\ , Y = y} = \sum_{\text{all }x} p(x,y).
 
twoski said:
So if that's the marginal PMF of x, then for y...

p_Y(y) = P\{ X \leq \infty\ , Y = y\} = \sum_{\text{all }x} p_{X,Y}(x,y).

Fixed it, and yes that is correct.
 
So using these 2 PMFs i have to determine whether X and Y are independent. Going by the definition i'd say they are independent.

Is this right?

E[X] = \sum_{\text{k}} x_k * p_X(x_k) = \sum_{\text{k}} x_k * \sum_{\text{x}} p(x,y).
 
Your ##E(X)## is right. For the next question, use the theorem: If ##X,Y## are independent, then ##E(XY) = E(X)E(Y)##.
 
twoski said:
So using these 2 PMFs i have to determine whether X and Y are independent. Going by the definition i'd say they are independent.

Is this right?

E[X] = \sum_{\text{k}} x_k * p_X(x_k) = \sum_{\text{k}} x_k * \sum_{\text{x}} p(x,y).

Your equation makes no sense: it is essentially summing over x twice, and not doing anything with y.
 

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