Poisson distribution questions

In summary, we are tasked to find the probability generating function and range of a Poisson distribution with parameter lambda, and then evaluate the expected value of a complex term involving 12 consecutive factors of x. Using the exponential power series, we found the probability generating function to be p_x (s) = exp(lamda(s-1)) and it is well defined for real values of s. To find the expected value, we can rewrite the given term as x!/(x-12)! and then use the definition of expected value. Simplifying the expression, we will be left with (x-12)! on the bottom, which may help us in our evaluation.
  • #1
silentone
4
0

Homework Statement


Suppose x has a Poisson [itex]\lambda[/itex] distribution

Find the probability generating function and range it is well defined. Then evaluate E[x(x-1)(x-2)(x-3)(x-4)(x-5)(x-6)(x-7)(x-7)(X-8)(x-9)(x-10)(x-11)]


Homework Equations


f_x (x) = exp(-lamda) (lamda)^x/x! for x=0,1,2,3...


The Attempt at a Solution


The probability generating function I got easily by using the exponential power series and got p_x (s) = exp(lamda(s-1)) . It is well defined for s real.

I do not know how to approach the expected value.
 
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  • #2
I could be wrong on this, so take this with a grain of salt.

Apply the definition of expected value. It looks like you could rewrite your term as x!/(x-12)! Does this help? Then when you take the sum, the x! should cancel, and you'll be left with (x-12)! on the bottom.
 

FAQ: Poisson distribution questions

1. What is the Poisson distribution?

The Poisson distribution is a discrete probability distribution that is used to model the number of times an event occurs in a fixed interval of time or space. It is often used to estimate the likelihood of rare events, such as accidents or natural disasters, happening within a given time frame.

2. What are the key assumptions of the Poisson distribution?

The key assumptions of the Poisson distribution are that the events are independent, the average rate of occurrence is constant, and the probability of an event occurring in a given interval is proportional to the length of the interval. Additionally, the events must be rare and mutually exclusive.

3. How is the Poisson distribution different from the binomial distribution?

The Poisson distribution is used to model the number of occurrences of a rare event in a fixed interval, while the binomial distribution is used to model the number of successes in a fixed number of trials. Additionally, the Poisson distribution assumes a constant rate of occurrence, while the binomial distribution allows for varying probabilities of success in each trial.

4. What is the mean and variance of the Poisson distribution?

The mean of the Poisson distribution is equal to the rate parameter, denoted as λ. The variance is also equal to λ, making the standard deviation equal to the square root of λ.

5. How is the Poisson distribution used in real-life scenarios?

The Poisson distribution is commonly used in insurance and risk assessment, where it can help estimate the likelihood of rare events such as car accidents or natural disasters. It is also used in the field of biology to model the number of mutations in a DNA sequence and in business to analyze customer arrivals and phone calls.

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