{ "id": "cond-mat/0609210", "version": "v1", "published": "2006-09-08T19:43:58.000Z", "updated": "2006-09-08T19:43:58.000Z", "title": "Analytical calculation of neighborhood order probabilities for high dimensional Poissonic processes and mean field models", "authors": [ "Cesar Augusto Sangaletti Tercariol", "Felipe de Mouta Kiipper", "Alexandre Souto Martinez" ], "comment": "12 pages, 3 figures and 1 table", "journal": "J. Phys. A: Math. Theor. 40 1981--1989 (2007)", "doi": "10.1088/1751-8113/40/9/005", "categories": [ "cond-mat.dis-nn", "cond-mat.stat-mech" ], "abstract": "Consider that the coordinates of $N$ points are randomly generated along the edges of a $d$-dimensional hypercube (random point problem). The probability that an arbitrary point is the $m$th nearest neighbor to its own $n$th nearest neighbor (Cox probabilities) plays an important role in spatial statistics. Also, it has been useful in the description of physical processes in disordered media. Here we propose a simpler derivation of Cox probabilities, where we stress the role played by the system dimensionality $d$. In the limit $d \\to \\infty$, the distances between pair of points become indenpendent (random link model) and closed analytical forms for the neighborhood probabilities are obtained both for the thermodynamic limit and finite-size system. Breaking the distance symmetry constraint drives us to the random map model, for which the Cox probabilities are obtained for two cases: whether a point is its own nearest neighbor or not.", "revisions": [ { "version": "v1", "updated": "2006-09-08T19:43:58.000Z" } ], "analyses": { "keywords": [ "high dimensional poissonic processes", "neighborhood order probabilities", "mean field models", "probability", "analytical calculation" ], "tags": [ "journal article" ], "note": { "typesetting": "TeX", "pages": 12, "language": "en", "license": "arXiv", "status": "editable" } } }