Anyone recognize this single parameter discrete probability distribution?

In summary, the conversation discusses a single parameter discrete probability distribution with a pmf defined over non-negative integers and involves the modified Bessel function of the first kind with order 0. The distribution's mean is known to be equal to L and as L approaches infinity, certain values approach specific approximations. However, the exact closed-form solution is still desired, as the asymptotic approximation is not practically useful. It is also mentioned that this distribution is a special case of the Conway-Maxwell-Poisson distribution with a non-Poisson parameter of 2.
  • #1
jacobcdf
5
0
I have a single parameter discrete probability distribution defined over the domain of non-negative integers with pmf in k of:

[tex]Pr(k;L) = \frac{L^{k}}{k! * k! * I_{0}(2*\sqrt{L})}[/tex]

Where [tex]I_{0}()[/tex] is the modified Bessel function of the first kind with order 0.

I do know that [tex]E(k^{2}) = L[/tex].

Can anyone come up with a closed form for the distribution mean?

Does anyone recognize this distribution?

Thanks in advance,
J.
 
Last edited:
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  • #2
I also know that as [tex]L \rightarrow \infty[/tex]:

[tex]\gamma_{1} \rightarrow \sqrt{\frac{1}{2*\sqrt{K}}[/tex]

and

[tex]\gamma_{2} \rightarrow \gamma_{1}^{2}[/tex]

and E(k) appears to approach something approximated by:

[tex]\sqrt{L - \frac{\sqrt{L}}{2}}[/tex]

But regardless, I still would like an exact closed-form solution, as the asymptotic approximation appears of little use practically.


J.
 
  • #3
In case anyone's interested, this distribution appears to be a special case of the Conway–Maxwell–Poisson distribution with the non-Poisson parameter [tex]\nu = 2[/tex].
 

1. What is a single parameter discrete probability distribution?

A single parameter discrete probability distribution is a type of probability distribution that is used to describe the likelihood of a discrete random variable taking on a specific value. It is characterized by a single parameter that controls the shape and behavior of the distribution, such as the mean or standard deviation.

2. How is a single parameter discrete probability distribution different from other types of distributions?

A single parameter discrete probability distribution differs from other types of distributions, such as continuous distributions, in that it is used to model discrete random variables. This means that the variable can only take on specific, distinct values rather than a continuous range of values.

3. What are some common examples of single parameter discrete probability distributions?

Some common examples of single parameter discrete probability distributions include the binomial distribution, which describes the number of successes in a fixed number of independent trials, and the Poisson distribution, which is often used to model the number of events occurring in a fixed time period.

4. How is the parameter of a single parameter discrete probability distribution determined?

The parameter of a single parameter discrete probability distribution is typically determined through statistical analysis of data. This involves calculating the mean and standard deviation of the data and then using these values to estimate the parameter that best fits the observed data.

5. What is the importance of understanding single parameter discrete probability distributions?

Understanding single parameter discrete probability distributions is important for many fields of science and research, as it allows for the accurate modeling and prediction of discrete events and outcomes. This can be useful in fields such as finance, biology, and engineering, among others.

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