scholarly journals Finite range decomposition for families of gradient Gaussian measures

2013 ◽  
Vol 264 (1) ◽  
pp. 169-206 ◽  
Author(s):  
Stefan Adams ◽  
Roman Kotecký ◽  
Stefan Müller
Keyword(s):  
Finite Range ◽  
Gaussian Measures ◽  
Finite Range Decomposition

Finite-Range Decomposition

2019 ◽  
pp. 37-52
Author(s):  
Roland Bauerschmidt ◽  
David C. Brydges ◽  
Gordon Slade
Keyword(s):  
Finite Range ◽  
Finite Range Decomposition

scholarly journals Finite Range Decomposition of Gaussian Processes

2004 ◽  
Vol 115 (1/2) ◽  
pp. 415-449 ◽  
Author(s):  
David C. Brydges ◽  
G. Guadagni ◽  
P. K. Mitter
Keyword(s):  
Gaussian Processes ◽  
Finite Range ◽  
Finite Range Decomposition

Finite-size instabilities in finite-range forces

2021 ◽  
Vol 103 (6) ◽  
Author(s):  
C. Gonzalez-Boquera ◽  
M. Centelles ◽  
X. Viñas ◽  
L. M. Robledo
Keyword(s):  
Finite Size ◽  
Finite Range

scholarly journals Markov properties of cluster processes

1996 ◽  
Vol 28 (2) ◽  
pp. 346-355 ◽  
Author(s):  
A. J. Baddeley ◽  
M. N. M. Van Lieshout ◽  
J. Møller
Keyword(s):  
Poisson Process ◽  
Point Process ◽  
Markov Property ◽  
Nearest Neighbour ◽  
Finite Range ◽  
Cluster Process ◽  
Cluster Point Process ◽  
Poisson Cluster ◽  
Markov Properties ◽  
Uniformly Bounded

We show that a Poisson cluster point process is a nearest-neighbour Markov point process [2] if the clusters have uniformly bounded diameter. It is typically not a finite-range Markov point process in the sense of Ripley and Kelly [12]. Furthermore, when the parent Poisson process is replaced by a Markov or nearest-neighbour Markov point process, the resulting cluster process is also nearest-neighbour Markov, provided all clusters are non-empty. In particular, the nearest-neighbour Markov property is preserved when points of the process are independently randomly translated, but not when they are randomly thinned.

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