Expectation of Negative Binomial Distribution

In summary, there are two interpretations of the random variable X for the negative binomial distribution: the number of failures or the number of trials needed to reach a predetermined number of successes. Both r/p and r(1-p)/p are correct expectations for these interpretations, respectively. Despite conflicting opinions, Wikipedia states that r(1-p)/p is the correct expectation. However, the speaker finds the proof for r/p from the geometric distribution to be convincing.
  • #1
GregA
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I am re-writing up some lecture notes and one of the proofs that E[X] for the negative binomial is r/p where r is the number of trials...The problem is there are a number of books that say r(1-p)/p is the correct expectation whilst others agree with 1/p

Which one is correct...for what its worth I have worked through the proof that the expectation for the geometric distribution is 1/p and find it pretty convincing...hence I'm lead to believe that r/p is correct for the negative binomial...I am worried though that a good number of sources differ in opinion however.

Can you folks pitch in please?
 
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  • #2
wikipedia says r(1-p)/p for E[X] for a Negative Binomial Distribution. I tend to trust it on math topics.

Are you sure you wrote down the proof correctly?
 
  • #3
Both are correct!

BTW, I think you are using a very non-standard definition. r is almost always the predetermined number of successes that must be reached, not the number of trials.

The reason both are correct is because there are two different interpretations of the random variable X: The number of failures that occur in reaching the r successes, or the number of trials needed to reach r successes. The expected value of the number of failures is [itex]r\frac{1-p}{p}[/itex] while the expected value of the number of trials is [itex]\frac r p[/itex].
 
  • #4
Excellent!...thanks for that..by the way I ought to have said successes but it was late when I typed it...again, thanks :)
 

What is the negative binomial distribution?

The negative binomial distribution is a probability distribution that describes the number of independent, identical events that occur before a specified number of successes are achieved. It is often used to model the number of trials needed to achieve a certain number of successes.

What is the difference between the negative binomial and binomial distributions?

The main difference between the negative binomial and binomial distributions is that the binomial distribution models the number of successes in a fixed number of trials, whereas the negative binomial distribution models the number of trials needed to achieve a certain number of successes. Additionally, the binomial distribution assumes a fixed probability of success for each trial, while the negative binomial distribution allows for varying probabilities of success.

What is the expected value of a negative binomial distribution?

The expected value, or mean, of a negative binomial distribution is equal to r(p), where r is the number of successes and p is the probability of success on each trial. In other words, it is the product of the number of successes and the probability of success on each trial.

How is the negative binomial distribution related to the Poisson distribution?

The negative binomial distribution is related to the Poisson distribution in that both describe the number of events that occur in a given time or space. However, the Poisson distribution assumes a fixed rate of occurrence, while the negative binomial distribution allows for varying rates of occurrence.

What is the practical application of negative binomial distribution in real life?

The negative binomial distribution is commonly used in fields such as biology, economics, and engineering to model the number of trials needed to achieve a certain number of successes. It is also used in insurance and risk management to model the number of claims or accidents that occur before a certain threshold is reached. Additionally, it can be used in sports analytics to model the number of games a team needs to win to make it to the playoffs.

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