Anyone recognize this single parameter discrete probability distribution?

jacobcdf
Messages
5
Reaction score
0
I have a single parameter discrete probability distribution defined over the domain of non-negative integers with pmf in k of:

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

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

I do know that E(k^{2}) = L.

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

Does anyone recognize this distribution?

Thanks in advance,
J.
 
Last edited:
Physics news on Phys.org
I also know that as L \rightarrow \infty:

\gamma_{1} \rightarrow \sqrt{\frac{1}{2*\sqrt{K}}

and

\gamma_{2} \rightarrow \gamma_{1}^{2}

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

\sqrt{L - \frac{\sqrt{L}}{2}}

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


J.
 
In case anyone's interested, this distribution appears to be a special case of the Conway–Maxwell–Poisson distribution with the non-Poisson parameter \nu = 2.
 
Namaste & G'day Postulate: A strongly-knit team wins on average over a less knit one Fundamentals: - Two teams face off with 4 players each - A polo team consists of players that each have assigned to them a measure of their ability (called a "Handicap" - 10 is highest, -2 lowest) I attempted to measure close-knitness of a team in terms of standard deviation (SD) of handicaps of the players. Failure: It turns out that, more often than, a team with a higher SD wins. In my language, that...
Hi all, I've been a roulette player for more than 10 years (although I took time off here and there) and it's only now that I'm trying to understand the physics of the game. Basically my strategy in roulette is to divide the wheel roughly into two halves (let's call them A and B). My theory is that in roulette there will invariably be variance. In other words, if A comes up 5 times in a row, B will be due to come up soon. However I have been proven wrong many times, and I have seen some...
Back
Top