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Poisson Numbers and Poisson Distributions in Subset Surprisology

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Abstract.

Given a family of k + 1 real-valued functions \(f_0 , \ldots ,f_k \) defined on the set \(\{ 1, \ldots ,n\} \) and measuring the intensity of certain signals, we want to investigate whether these functions are ‘dependent’ or ‘independent’ by checking whether, for some given family of threshold values \(T_0 , \ldots ,T_k ,\) the size a of the collection of numbers \(j \in \{ 1, \ldots ,n\} \) whose signals \(f_0 (j), \ldots ,f_k (j)\) exceed the corresponding threshold values \(T_0 , \ldots ,T_k \) simultaneously for all \(0, \ldots ,k\) is surprisingly large (or small) in comparison to the family of cardinalities

$$ a_i : = \# \{ j \in \{ 1, \ldots ,n\} |f_i (j) > T_i \} \;(i = 0, \ldots ,k) $$

of those numbers \(1 \leq j \leq n\) whose signals f i (j) individually exceed, for a given index i, the corresponding threshold value T i . Such problems turn presently up in topological proteomics, a new direction of protein-interaction research that has become feasible due to new techniques developed in fluorescence microscopy called Multi-Epitope Ligand Cartography (or, for short, MELK = Multi-Epitop Liganden Kartographie). The above problem has led us to study the numbers \(A_{n|a_0 , \ldots ,a_k } (a)\) of families of subsets \(A_0 ,A_1 , \ldots ,A_k \) of \(\{ 1, \ldots ,n\} \) with \(\# A_i = a_i \) for all \(i = 0, \ldots ,k\) and \(\# \bigcap\nolimits_{i = 0, \ldots k} {A_i = a,} \) and to investigate their asymptotic behaviour. In this note, we show that the associated probability distributions

$$ p_{n|a_0 , \ldots ,a_k } = \left( {p_{n|a_0 , \ldots ,a_k } (a)} \right)_{a \in {\text{N}}_0 } $$

defined on the set \({\mathbf{N}}_0 \) of non-negative integers by

$$ p_{n|a_0 , \ldots ,a_k } (a): = \frac{{A_{n|a_0 , \ldots ,a_k } (a)}} {{\prod\nolimits_{i = 0}^k {\left( {\begin{array}{*{20}c} n \\ {a_i } \\ \end{array} } \right)} }}\quad (a \in {\text{N}}_0 ) $$

converge, with n → ∞, towards the Poisson distribution

$$ {\text{poiss}}_\alpha = ({\text{poiss}}_\alpha (a))_{a \in {\mathbf{N}}_0 } $$

for some fixed \(\alpha \in {\mathbf{R}}_{ > 0} \) provided the numbers \(a_i \,(i = 0, \ldots ,k)\) are assumed to converge with n to infinity in such a way that the conditions

$$ a_i \leq n\quad {\text{and}}\quad \mathop {\lim }\limits_{n \to \infty } \frac{{\prod\nolimits_{i = 0}^k {a_i } }} {{n^k }} = \alpha $$

are satisfied. Remarkably, it is the alternating signs in the expressions for \(A_{n|a_0 , \ldots ,a_k } (a)\) resulting from the standard exclusion-inclusion principle that correspond to the alternating signs in the power series expression for exp(−α) when n turns to infinity.

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Correspondence to Andreas W. M. Dress.

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Received November 19, 2004

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Dress, A.W.M., Lokot, T., Pustyl’nikov, L.D. et al. Poisson Numbers and Poisson Distributions in Subset Surprisology. Ann. Comb. 8, 473–485 (2005). https://doi.org/10.1007/s00026-004-0234-2

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  • DOI: https://doi.org/10.1007/s00026-004-0234-2

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