Poisson PDF with non-integer support

In summary, the problem involves finding the probability that a Poisson random variable with lambda = 2 is greater than 0.5. Using the cumulative distribution function for Poisson and the incomplete gamma function, the solution can be found. This method can be applied to other similar probability problems.
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Oxymoron
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Homework Statement


If [tex]X[/tex] is a Poisson random variable with [tex]\lambda = 2[/tex] find the probability that [tex]X>0.5[/tex].


Homework Equations


The Poisson PDF:
[tex]P(x,\lambda) = \frac{\lambda^k}{k!}e^{-\lambda} [/tex]



The Attempt at a Solution


Usually with these sorts of probability problems where they ask you to find the probability that [tex]x[/tex] is larger than some number [tex]n[/tex] I use the CDF of the PDF and write

[tex]P(X_{PDF}>n) = 1-P(X_{PDF}\leq n) = 1-P(X_{CDF}=n)[/tex]

However, I am at a loss with the Poisson distribution because the CDF involves the gamma function. I can do it on Maple where I define

[tex]\mbox{Poi}(\lambda,x) := \sum_{t=0}^x \frac{\lambda^t}{t!}e^{-\lambda}[/tex]

and then calculate

[tex]1-\mbox{evalf}(\mbox{Poi}(2,0.5)) = 0.7385... [/tex]

Also, if I try to use z-scores in a Poisson table the values for x are all integers, am I meant to use interpolation? Or is there an algebraic way of solving this?
 
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  • #2
Solved.

I used the cumulative distribution function for Poisson:

[tex]F(t,\lambda) = \frac{\Gamma\left(\lfloor k+1 \rfloor,\lambda\right)}{\lfloor k \rfloor!}[/tex]

and used the incomplete gamma function

[tex]\Gamma(k,x) = \int_x^{\infty}t^{k-1}e^t\mbox{d}t[/tex]

and integrated by parts twice (twice because the support is [tex]\lambda = 2[/tex] by the way!) to find an answer. It turns out that non-integers can be put into the gamma function, but it just floors them anyway. Did it on Maple as well as by hand and it works.
 

What is a Poisson PDF with non-integer support?

A Poisson PDF (Probability Density Function) with non-integer support is a mathematical function that describes the probability of a discrete random variable taking on a certain value when the variable follows a Poisson distribution. Unlike a standard Poisson PDF, which has support only for integer values, a Poisson PDF with non-integer support can accommodate non-integer values as well.

How is a Poisson PDF with non-integer support different from a standard Poisson PDF?

A standard Poisson PDF has support only for integer values, while a Poisson PDF with non-integer support can accommodate non-integer values as well. This makes it a more versatile tool for modeling real-world phenomena where the variable of interest may not necessarily take on integer values.

What are some examples of real-world phenomena that can be modeled using a Poisson PDF with non-integer support?

A Poisson PDF with non-integer support can be used to model a variety of real-world phenomena, such as the number of accidents in a given time period, the number of customers arriving at a store in a given hour, or the number of earthquakes in a specific region over a certain period of time.

How is a Poisson PDF with non-integer support calculated?

A Poisson PDF with non-integer support can be calculated using the formula: P(x;λ) = (λ^x * e^-λ) / x!, where x is the non-integer value of interest and λ is the parameter of the Poisson distribution.

What are the applications of a Poisson PDF with non-integer support in scientific research?

A Poisson PDF with non-integer support can be a useful tool in various scientific fields, such as epidemiology, ecology, and economics. It can help researchers understand and predict patterns and trends in data that involve non-integer values, and can provide valuable insights for decision-making and policy-making processes.

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