50 research outputs found
Connectivity in Sub-Poisson Networks
We consider a class of point processes (pp), which we call {\em sub-Poisson};
these are pp that can be directionally-convexly () dominated by some
Poisson pp. The order has already been shown useful in comparing various
point process characteristics, including Ripley's and correlation functions as
well as shot-noise fields generated by pp, indicating in particular that
smaller in the order processes exhibit more regularity (less clustering,
less voids) in the repartition of their points. Using these results, in this
paper we study the impact of the ordering of pp on the properties of two
continuum percolation models, which have been proposed in the literature to
address macroscopic connectivity properties of large wireless networks. As the
first main result of this paper, we extend the classical result on the
existence of phase transition in the percolation of the Gilbert's graph (called
also the Boolean model), generated by a homogeneous Poisson pp, to the class of
homogeneous sub-Poisson pp. We also extend a recent result of the same nature
for the SINR graph, to sub-Poisson pp. Finally, as examples we show that the
so-called perturbed lattices are sub-Poisson. More generally, perturbed
lattices provide some spectrum of models that ranges from periodic grids,
usually considered in cellular network context, to Poisson ad-hoc networks, and
to various more clustered pp including some doubly stochastic Poisson ones.Comment: 8 pages, 10 figures, to appear in Proc. of Allerton 2010. For an
extended version see http://hal.inria.fr/inria-00497707 version
Clustering comparison of point processes with applications to random geometric models
In this chapter we review some examples, methods, and recent results
involving comparison of clustering properties of point processes. Our approach
is founded on some basic observations allowing us to consider void
probabilities and moment measures as two complementary tools for capturing
clustering phenomena in point processes. As might be expected, smaller values
of these characteristics indicate less clustering. Also, various global and
local functionals of random geometric models driven by point processes admit
more or less explicit bounds involving void probabilities and moment measures,
thus aiding the study of impact of clustering of the underlying point process.
When stronger tools are needed, directional convex ordering of point processes
happens to be an appropriate choice, as well as the notion of (positive or
negative) association, when comparison to the Poisson point process is
considered. We explain the relations between these tools and provide examples
of point processes admitting them. Furthermore, we sketch some recent results
obtained using the aforementioned comparison tools, regarding percolation and
coverage properties of the Boolean model, the SINR model, subgraph counts in
random geometric graphs, and more generally, U-statistics of point processes.
We also mention some results on Betti numbers for \v{C}ech and Vietoris-Rips
random complexes generated by stationary point processes. A general observation
is that many of the results derived previously for the Poisson point process
generalise to some "sub-Poisson" processes, defined as those clustering less
than the Poisson process in the sense of void probabilities and moment
measures, negative association or dcx-ordering.Comment: 44 pages, 4 figure
Limit laws for k-coverage of paths by a Markov-Poisson-Boolean model
Let P := {X_i,i >= 1} be a stationary Poisson point process in R^d, {C_i,i >=
1} be a sequence of i.i.d. random sets in R^d, and {Y_i^t; t \geq 0, i >= 1} be
i.i.d. {0,1}-valued continuous time stationary Markov chains. We define the
Markov-Poisson-Boolean model C_t := {Y_i^t(X_i + C_i), i >= 1}. C_t represents
the coverage process at time t. We first obtain limit laws for k-coverage of an
area at an arbitrary instant. We then obtain the limit laws for the k-coverage
seen by a particle as it moves along a one-dimensional path.Comment: 1 figure. 24 Pages. Accepted at Stochastic Models. Theorems 6 and 7
corrected. Theorem 9 and Appendix adde
Limit theory for geometric statistics of point processes having fast decay of correlations
Let be a simple,stationary point process having fast decay of
correlations, i.e., its correlation functions factorize up to an additive error
decaying faster than any power of the separation distance. Let be its restriction to windows . We consider the statistic where denotes a score function
representing the interaction of with respect to . When depends
on local data in the sense that its radius of stabilization has an exponential
tail, we establish expectation asymptotics, variance asymptotics, and CLT for
and, more generally, for statistics of the re-scaled, possibly
signed, -weighted point measures , as . This gives the
limit theory for non-linear geometric statistics (such as clique counts,
intrinsic volumes of the Boolean model, and total edge length of the
-nearest neighbors graph) of -determinantal point processes having
fast decreasing kernels extending the CLTs of Soshnikov (2002) to non-linear
statistics. It also gives the limit theory for geometric U-statistics of
-permanental point processes and the zero set of Gaussian entire
functions, extending the CLTs of Nazarov and Sodin (2012) and Shirai and
Takahashi (2003), which are also confined to linear statistics. The proof of
the central limit theorem relies on a factorial moment expansion originating in
Blaszczyszyn (1995), Blaszczyszyn, Merzbach, Schmidt (1997) to show the fast
decay of the correlations of -weighted point measures. The latter property
is shown to imply a condition equivalent to Brillinger mixing and consequently
yields the CLT for via an extension of the cumulant method.Comment: 62 pages. Fundamental changes to the terminology including the title.
The earlier 'clustering' condition is now introduced as a notion of mixing
and its connection to Brillinger mixing is remarked. Newer results for
superposition of independent point processes have been adde
Central limit theorem for exponentially quasi-local statistics of spin models on Cayley graphs
Central limit theorems for linear statistics of lattice random fields
(including spin models) are usually proven under suitable mixing conditions or
quasi-associativity. Many interesting examples of spin models do not satisfy
mixing conditions, and on the other hand, it does not seem easy to show central
limit theorem for local statistics via quasi-associativity. In this work, we
prove general central limit theorems for local statistics and exponentially
quasi-local statistics of spin models on discrete Cayley graphs with polynomial
growth. Further, we supplement these results by proving similar central limit
theorems for random fields on discrete Cayley graphs and taking values in a
countable space but under the stronger assumptions of {\alpha}-mixing (for
local statistics) and exponential {\alpha}-mixing (for exponentially
quasi-local statistics). All our central limit theorems assume a suitable
variance lower bound like many others in the literature. We illustrate our
general central limit theorem with specific examples of lattice spin models and
statistics arising in computational topology, statistical physics and random
networks. Examples of clustering spin models include quasi-associated spin
models with fast decaying covariances like the off-critical Ising model, level
sets of Gaussian random fields with fast decaying covariances like the massive
Gaussian free field and determinantal point processes with fast decaying
kernels. Examples of local statistics include intrinsic volumes, face counts,
component counts of random cubical complexes while exponentially quasi-local
statistics include nearest neighbour distances in spin models and Betti numbers
of sub-critical random cubical complexes.Comment: Minor changes incorporated based on suggestions by referee