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The STIT tessellation process was introduced and examined by Mecke, Nagel and Wei{\ss}; many of its main characteristics are contained in a paper published by Nagel and Wei{\ss} in 2005. In a paper published in 2010, Mecke introduced…

Probability · Mathematics 2011-06-02 Eike Biehler

For a class of cell division processes, generating tessellations of the Euclidean space $\mathbb{R}^d$, spatial consistency is investigated. This addresses the problem whether the distribution of these tessellations, restricted to a bounded…

Probability · Mathematics 2013-12-03 Werner Nagel , Eike Biehler

Processes of random tessellations of the Euclidean space $\mathbb{R}^d$, $d\geq 1$, are considered which are generated by subsequent division of their cells. Such processes are characterized by the laws of the life times of the cells until…

Probability · Mathematics 2024-05-08 Servet Martínez , Werner Nagel

Spatially homogeneous random tessellations that are stable under iteration (nesting) in the 3-dimensional Euclidean space are considered, so-called STIT tessellations. They arise as outcome of a spatio-temporal process of subsequent cell…

Probability · Mathematics 2013-09-20 Christoph Thaele , Viola Weiss

An analogue of the classical Mecke formula for Poisson point processes is proved for the class of space-time STIT tessellation processes. From this key identity the Markov property of a class of associated random processes is derived. This…

Probability · Mathematics 2017-11-06 Werner Nagel , Linh Ngoc Nguyen , Christoph Thaele , Viola Weiss

Three-dimensional random tessellations that are stable under iteration (STIT tessellations) are considered. They arise as a result of subsequent cell division, which implies that their cells are not face-to-face. The edges of the…

Probability · Mathematics 2012-09-26 Christoph Thaele , Viola Weiss , Werner Nagel

In this paper we consider a random partition of the plane into cells, the partition being based on the nodes and links of a {\it random planar geometric graph}. The resulting structure generalises the \emph{random \tes}\ hitherto studied in…

Probability · Mathematics 2016-06-07 Richard Cowan , Albert K. L. Tsang

This paper deals with iteration stable (STIT) tessellations, and, more generally, with a certain class of tessellations that are infinitely divisible with respect to iteration. They form a new, rich and flexible class of spatio-temporal…

Probability · Mathematics 2013-03-04 Tomasz Schreiber , Christoph Thaele

For cell-division processes in a window, Cowan introduced four selection rules and two division rules each of which stands for one cell-division model. One of these is the area-weighted in-cell model. In this model, each cell is selected…

Probability · Mathematics 2012-04-16 Eike Biehler

Since the seminal work of Mecke, Nagel and Weiss, the iteration stable (STIT) tessellations have attracted considerable interest in stochastic geometry as a natural and flexible yet analytically tractable model for hierarchical spatial…

Probability · Mathematics 2015-03-13 Tomasz Schreiber , Christoph Thaele

In this paper, we study a distribution of labeled particles on a continuous ring. It arises in three different ways, all related to the multi-type TASEP on a ring. We prove formulas for the probability density function for some permutations…

Combinatorics · Mathematics 2017-03-28 Erik Aas , Svante Linusson

In their 2002 paper, Ciucu and Krattenthaler proved several product formulas for the number of lozenge tilings of various regions obtained from a centrally symmetric hexagon on the triangular lattice by removing maximal staircase regions…

Combinatorics · Mathematics 2013-09-19 Mihai Ciucu , Ilse Fischer

Stationary and isotropic iteration stable random tessellations are considered, which can be constructed by a random process of cell division. The collection of maximal polytopes at a fixed time $t$ within a convex window $W\subset{\Bbb…

Probability · Mathematics 2011-04-05 Tomasz Schreiber , Christoph Thaele

A random recursive cell splitting scheme of the $2$-dimensional unit sphere is considered, which is the spherical analogue of the STIT tessellation process from Euclidean stochastic geometry. First-order moments are computed for a large…

Probability · Mathematics 2017-11-06 Christian Deuß , Julia Hörrmann , Christoph Thaele

A new and rather broad class of stationary (i.e. stochastically translation invariant) random tessellations of the $d$-dimensional Euclidean space is introduced, which are called shape-driven nested Markov tessellations. Locally, these…

Probability · Mathematics 2013-09-16 Tomasz Schreiber , Christoph Thaele

Since the seminal work by Nagel and Weiss, the iteration stable (STIT) tessellations have attracted considerable interest in stochastic geometry as a natural and flexible, yet analytically tractable model for hierarchical spatial…

Probability · Mathematics 2014-12-25 Tomasz Schreiber , Christoph Thaele

Beginning from the resolution of the Dirichlet L function, using the inner product formula between two infinite-dimensional vectors in the complex space, the author proved the baffling problem--Hecke conjecture.

General Mathematics · Mathematics 2007-05-23 Kaida Shi

The classical honeycomb conjecture asserts that any partition of the plane into regions of equal area has perimeter at least that of the regular hexagonal honeycomb tiling. Pappus discusses this problem in his preface to Book V. This paper…

Metric Geometry · Mathematics 2007-05-23 Thomas C. Hales

The intent of this paper is to describe the large scale asymptotic geometry of iteration stable (STIT) tessellations in $\mathbb{R}^d$, which form a rather new, rich and flexible class of random tessellations considered in stochastic…

Probability · Mathematics 2014-12-25 Tomasz Schreiber , Christoph Thaele

A Gilbert tessellation arises by letting linear segments (cracks) in the plane unfold in time with constant speed, starting from a homogeneous Poisson point process of germs in randomly chosen directions. Whenever a growing edge hits an…

Probability · Mathematics 2010-05-04 Tomasz Schreiber , Natalia Soja
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