Brownian Survival in a Clusterized Trapping Medium

1998 ◽  
Vol 10 (02) ◽  
pp. 147-189 ◽  
Author(s):  
Sergio Albeverio ◽  
Leonid V. Bogachev
Keyword(s):  
Brownian Particle ◽  
Cluster Process ◽  
Poisson Cluster Process ◽  
Cluster Point Process ◽  
Long Time ◽  
Fkg Inequalities ◽  
Poisson Cluster ◽  
Random Traps ◽  
Physical Literature ◽  
Trapping Process

The survival problem for a Brownian particle moving among random traps is considered in the case where the traps are correlated in a particular fashion being gathered in clusters. It is assumed that the clusters are statistically identical and independent of each other and are distributed in space according to a Poisson law. Mathematically, such a trapping medium is described via a Poisson cluster point process. We prove that the particle survival probability is increased at all times as compared to the case of noncorrelated (Poissonian) traps, which implies the slowdown of the trapping process. It is shown that this effect may be interpreted as the manifestation of the trap "attraction", thus supporting assertions on the qualitative influence of the trap "interaction" on the trapping rate claimed earlier in the physical literature. The long-time survival asymptotics (of Donsker–Varadhan type) is also derived. By way of appendix, FKG inequalities for certain functionals are proven and the limiting distribution for a Poisson cluster process, under clusters' scaling, is determined.

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.

Cluster shock models

1981 ◽  
Vol 18 (01) ◽  
pp. 104-111 ◽  
Author(s):  
Peter F. Thall
Keyword(s):  
Failure Rate ◽  
Present Note ◽  
Cluster Process ◽  
Completely Monotonic ◽  
Poisson Cluster Process ◽  
Preservation Theorem ◽  
Shock Models ◽  
Poisson Cluster ◽  
Decreasing Failure Rate ◽  
Over Time

The survival distribution of a device subject to a sequence of shocks occurring randomly over time is studied by Esary, Marshall and Proschan (1973) and by A-Hameed and Proschan (1973), (1975). The present note treats the case in which shocks occur according to a homogeneous Poisson cluster process. It is shown that if[the device surviveskshocks] =zk, 0 <z< 1, then the device exhibits a decreasing failure rate. A DFR preservation theorem is proved for completely monotonic. A counterexample to the IFR preservation theorem is given in whichis strictly IFR while the failure rate is initially decreasing and then increasing.

Count distributions, orderliness and invariance of Poisson cluster processes

1979 ◽  
Vol 16 (02) ◽  
pp. 261-273 ◽  
Author(s):  
Larry P. Ammann ◽  
Peter F. Thall
Keyword(s):  
Present Article ◽  
Generating Functional ◽  
Cluster Process ◽  
Multiple Events ◽  
Poisson Cluster Process ◽  
Finite Dimensional ◽  
The Family ◽  
Moment Measures ◽  
Poisson Cluster ◽  
Cluster Processes

The probability generating functional (p.g.fl.) of a non-homogeneous Poisson cluster process is characterized in Ammann and Thall (1977) via a decomposition of the KLM measure of the process. This p.g.fl. representation is utilized in the present article to show that the family 𝒟 of Poisson cluster processes with a.s. finite clusters is invariant under a class of cluster transformations. Explicit expressions for the finite-dimensional count distributions, product moment measures, and the distribution of clusters are derived in terms of the KLM measure. It is also shown that an element of 𝒟 has no multiple events iff the points of each cluster are a.s. distinct.

scholarly journals On customer flows in jackson queueing networks

2010 ◽  
Vol 42 (04) ◽  
pp. 1094-1101
Author(s):  
Sen Tan ◽  
Aihua Xia
Keyword(s):  
Poisson Distribution ◽  
Queueing Networks ◽  
Queueing Network ◽  
Equilibrium Flow ◽  
Small Probability ◽  
Flow Process ◽  
Cluster Process ◽  
Poisson Cluster Process ◽  
Poisson Cluster ◽  
Approximate Version

Melamed's theorem states that, for a Jackson queueing network, the equilibrium flow along a link follows a Poisson distribution if and only if no customers can travel along the link more than once. Barbour and Brown (1996) considered the Poisson approximate version of Melamed's theorem by allowing the customers a small probability p of travelling along the link more than once. In this note, we prove that the customer flow process is a Poisson cluster process and then establish a general approximate version of Melamed's theorem that accommodates all possible cases of 0 ≤ p < 1.

Cluster shock models

1981 ◽  
Vol 18 (1) ◽  
pp. 104-111 ◽  
Author(s):  
Peter F. Thall
Keyword(s):  
Failure Rate ◽  
Present Note ◽  
Cluster Process ◽  
Completely Monotonic ◽  
Poisson Cluster Process ◽  
Preservation Theorem ◽  
Shock Models ◽  
Poisson Cluster ◽  
Decreasing Failure Rate ◽  
Over Time

The survival distribution of a device subject to a sequence of shocks occurring randomly over time is studied by Esary, Marshall and Proschan (1973) and by A-Hameed and Proschan (1973), (1975). The present note treats the case in which shocks occur according to a homogeneous Poisson cluster process. It is shown that if [the device survives k shocks] = zk, 0 < z < 1, then the device exhibits a decreasing failure rate. A DFR preservation theorem is proved for completely monotonic . A counterexample to the IFR preservation theorem is given in which is strictly IFR while the failure rate is initially decreasing and then increasing.

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