Probability generating function (binomial distribution)

In summary, the probability generating function G is defined for counting random variables with a range of {0,1,2,3,...} and is represented by G(s) = E(s^{y}) for s values where E(|s|^{y}) < \infty. It can also be represented as G(s) = [(1-p) + ps]^{n} where q = 1-p.
  • #1
SolidSnake
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Homework Statement


The probabilty generating funtion G is definied for random varibles whos range are [tex]\subset[/tex] {0,1,2,3,...}. If Y is such a random variable we will call it a counting random varible. Its probabiltiy generating function is [tex]G(s) = E(s^{y}) [/tex] for those s's such that [tex]E(|s|^{y})[/tex]) < [tex]\infty[/tex].

Homework Equations



binomial distribution = [tex]\left(\stackrel{n}{y}\right)[/tex][tex]p^{y}[/tex][tex]q^{n-y}[/tex] , y = 0,1,2,3,...n and 0 [tex]\leq[/tex] p [tex]\leq[/tex] 1

The Attempt at a Solution



What i have so far is...

[tex]G(s) = E(s^{y}) [/tex] = [tex]\sum[/tex] [tex]s^{y}[/tex][tex]\left(\stackrel{n}{y}\right)[/tex][tex]p^{y}[/tex][tex]q^{n-y}[/tex]

[tex]G(s) = E(s^{y}) [/tex] = [tex]\sum[/tex] [tex]\left(\stackrel{n}{y}\right)[/tex][tex](sp)^{y}[/tex][tex]q^{n-y}[/tex]

not sure where to go from that. i managed to do it for the geometric random variable distribution b/c there was no "n choose y". Thanks to wiki, I know what the answer should be. The answer is G(s) = [tex][(1-p) + ps]^{n}[/tex]. I can't see how they went from what i have above to that.
 
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  • #2
well i just realized that [tex]G(s) = E(s^{y}) [/tex] = [tex]\sum[/tex] [tex]\left(\stackrel{n}{y}\right)[/tex][tex](sp)^{y}[/tex][tex]q^{n-y}[/tex]

is the same thing as [tex](q + sp)^{n}[/tex] .

Also by definition [tex] p + q = 1 \Rightarrow q = 1-p [/tex] which means...

[tex]G(s) = E(s^{y}) = [(1-p) + ps]^{n} [/tex]
 
Last edited:

1. What is a probability generating function?

A probability generating function (PGF) is a mathematical function that is used to describe the probability distribution of a discrete random variable. It is defined as the sum of the probabilities of each possible outcome multiplied by its corresponding variable raised to that outcome's power.

2. What is the binomial distribution?

The binomial distribution is a type of probability distribution that describes the number of successes in a fixed number of independent trials with a binary outcome (e.g. success or failure). It is characterized by two parameters: n, the number of trials, and p, the probability of success in each trial.

3. How is the probability generating function used in the binomial distribution?

The probability generating function for a binomial distribution is given by the formula G(t) = (q + pt)^n, where q = 1-p. This function can be used to calculate the probability of obtaining a specific number of successes in n trials, or to find the expected value and variance of the distribution.

4. What are the properties of a probability generating function?

The probability generating function has several important properties. First, it always exists for any random variable with a finite range. Second, it is always non-negative and monotonically increasing. Third, the value of the function at t = 1 is equal to 1. Fourth, the k-th derivative of the function evaluated at t = 0 is equal to the probability of k successes in n trials.

5. How is the probability generating function related to other probability distributions?

The probability generating function is closely related to other probability distributions, such as the Poisson distribution and the negative binomial distribution. In fact, the binomial distribution can be thought of as a special case of the negative binomial distribution, with a fixed number of trials. Additionally, the moments of a distribution can be calculated using the probability generating function, making it a useful tool in probability and statistics.

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