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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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.
 

Related to Poisson PDF with non-integer support

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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