Probability Mass Functions of Binomial Variables

In summary, the problem requires finding the PMF of the sum of two independent binomial random variables with parameters n and p. The PMF of X and Y is given by (n C k)pk(1-p)n-k. To find the PMF of X+Y, we can simply flip a coin 2n times with probability of heads p. There is no need for convolution in this case.
  • #1
Kalinka35
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Homework Statement


Let X and Y be independent binomial random variables with parameters n and p.
Find the PMF of X+Y.
Find the conditional PMF of X given that X+Y=m.


Homework Equations


The PMF of X is P(X=k)=(n C k)pk(1-p)n-k
The PMF for Y would be the same.

The Attempt at a Solution


I am really not sure how to go about solving this problem though I have been told that the first part can be done with no computation or calculations at all. Not sure at all on the second part.
 
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  • #2
Do you know anything in general about the PMF of the sum of two independent discrete random variables? Does "convolution" ring a bell?

If not, don't worry about it. Think about what a binomial distribution with parameters n and p means. You can think of it as the number of "heads" resulting from flipping a coin n times, where the probability of "head" is p. If you do that experiment TWICE, independently, and the results are X and Y, respectively, then X + Y is equivalent to simply flipping the coin 2n times, right? What's the PMF for that experiment?
 
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  • #3
Hmm, not that I know of.
I haven't heard the term convolution before.
Could you provide me with a definition?
 
  • #4
Kalinka35 said:
Hmm, not that I know of.
I haven't heard the term convolution before.
Could you provide me with a definition?

You need it to answer the question in general (for arbitrary PMFs), but don't worry about it in this case - for this particular example you can solve it another way (see my edited post above).
 
  • #5
Ah yes, I understand it now.
Thanks very much for your clear explanation.
 

FAQ: Probability Mass Functions of Binomial Variables

What is a probability mass function (PMF)?

A probability mass function (PMF) is a statistical measure that describes the probability distribution of a discrete random variable. It assigns a probability to each possible outcome of the variable, such as the number of successes in a certain number of trials.

What is a binomial variable?

A binomial variable is a type of discrete random variable that has only two possible outcomes: success or failure. It is often used to model situations such as flipping a coin or counting the number of heads in a certain number of coin flips.

How is a PMF of a binomial variable calculated?

The PMF of a binomial variable can be calculated using the binomial probability formula: P(X=x) = (n choose x) * p^x * (1-p)^(n-x), where n is the number of trials, x is the number of successes, and p is the probability of success in a single trial.

What is the difference between a PMF and a cumulative distribution function (CDF)?

A PMF gives the probability of each individual outcome of a discrete random variable, while a CDF gives the probability that the variable takes on a value less than or equal to a given value. In other words, a PMF provides a probability for each possible outcome, while a CDF provides a cumulative probability for all outcomes up to a certain point.

How can PMFs of binomial variables be used in real-life applications?

PMFs of binomial variables can be used in various real-life applications, such as in market research to estimate the success rate of a new product, in quality control to determine the defect rate of a production process, and in medical studies to analyze the effectiveness of a treatment. They can also be used to model and predict outcomes in games of chance, such as gambling or sports.

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