Searcharxiv⌕ Search

arXiv subjects

Pablo A. Ferrari

Publications and source records attributed to Pablo A. Ferrari.

At least 55 records · Page 3Linked to original sources

A slow-to-start traffic model related to a M/M/1 queue

We consider a system of ordered cars moving in $\R$ from right to left. Each car is represented by a point in $\R$; two or more cars can occupy the same point but cannot overpass. Cars have two possible velocities: either 0 or 1. An unblocked car needs an exponential random time of mean 1 to pass from speed 0 to speed 1 (\emph{slow-to-start}). Car $i$, say, travels at speed 1 until it (possibly) hits the stopped car $i-1$ to its left. After the departure of car $i-1$, car $i$ waits an exponential random time to change its speed to 1, travels at this speed until it hits again stopped car $i-1$ and so on. Initially cars are distributed in $\R$ according to a Poisson process of parameter $λ<1$. We show that every car will be stopped only a finite number of times and that the final relative car positions is again a Poisson process with parameter $λ$. To do that, we relate the trajectories of the cars to a $M/M/1$ stationary queue as follows. Space in the traffic model is time for the queue. The initial positions of the cars coincide with the arrival process of the queue and the final relative car positions match the departure process of the queue.

cond-mat.stat-mech↗

Limit theorems for sequences of random trees

We consider a random tree and introduce a metric in the space of trees to define the ``mean tree'' as the tree minimizing the average distance to the random tree. When the resulting metric space is compact we have laws of large numbers and central limit theorems for sequence of independent identically distributed random trees. As application we propose tests to check if two samples of random trees have the same law.

math.PR↗

Condensation for a fixed number of independent random variables

A family of m independent identically distributed random variables indexed by a chemical potential ϕ\in[0,γ] represents piles of particles. As ϕincreases to γ, the mean number of particles per site converges to a maximal density ρ_c<\infty. The distribution of particles conditioned on the total number of particles equal to n does not depend on ϕ(canonical ensemble). For fixed m, as n goes to infinity the canonical ensemble measure behave as follows: removing the site with the maximal number of particles, the distribution of particles in the remaining sites converges to the grand canonical measure with density ρ_c; the remaining particles concentrate (condensate) on a single site.

math.PR↗

Hitting times for independent random walks on $\mathbb{Z}^d$

We consider a system of asymmetric independent random walks on $\mathbb{Z}^d$, denoted by $\{η_t,t\in{\mathbb{R}}\}$, stationary under the product Poisson measure $ν_ρ$ of marginal density $ρ>0$. We fix a pattern $\mathcal{A}$, an increasing local event, and denote by $τ$ the hitting time of $\mathcal{A}$. By using a loss network representation of our system, at small density, we obtain a coupling between the laws of $η_t$ conditioned on $\{τ>t\}$ for all times $t$. When $d\ge3$, this provides bounds on the rate of convergence of the law of $η_t$ conditioned on $\{τ>t\}$ toward its limiting probability measure as $t$ tends to infinity. We also treat the case where the initial measure is close to $ν_ρ$ without being product.

math.PR↗

Quasi stationary distributions and Fleming-Viot processes in countable spaces

We consider an irreducible pure jump Markov process with rates Q=(q(x,y)) on Λ\cup\{0\} with Λcountable and 0 an absorbing state. A quasi-stationary distribution (qsd) is a probability measure νon Λthat satisfies: starting with ν, the conditional distribution at time t, given that at time t the process has not been absorbed, is still ν. That is, ν(x) = νP_t(x)/(\sum_{y\inΛ}νP_t(y)), with P_t the transition probabilities for the process with rates Q. A Fleming-Viot (fv) process is a system of N particles moving in Λ. Each particle moves independently with rates Q until it hits the absorbing state 0; but then instantaneously chooses one of the N-1 particles remaining in Λand jumps to its position. Between absorptions each particle moves with rates Q independently. Under the condition α:=\sum_x\inf Q(\cdot,x) > \sup Q(\cdot,0):=C we prove existence of qsd for Q; uniqueness has been proven by Jacka and Roberts. When α>0 the {\fv} process is ergodic for each N. Under α>C the mean normalized densities of the fv unique stationary measure converge to the qsd of Q, as N \to \infty; in this limit the variances vanish.

math.PR↗

Roughening and inclination of competition interfaces

The competition interface between two growing ``Young clusters'' (diagrams), in a two-dimensional random cone, is mapped to the path of a second-class particle in the one-dimensional totally asymmetric simple exclusion process. Using the asymptotics of the second class particle and hydrodynamic limits for the exclusion process (Burgers equation), we show that the behavior of the competition interface depends on the angle of the cone: for angles in [180^o, 270^o) the competition interface has a deterministic inclination, while for angles in [90^o,180^o) the inclination is random. We relate the competition model to a model of random directed polymers, and obtain some partial results for the fluctuations of the competition interface.

math.PR↗

Harness processes and harmonic crystals

In the Hammersley harness processes the real-valued height at each site i in Z^d is updated at rate 1 to an average of the neighboring heights plus a centered random variable (the noise). We construct the process "a la Harris" simultaneously for all times and boxes contained in Z^d. With this representation we compute covariances and show L^2 and almost sure time and space convergence of the process. In particular, the process started from the flat configuration and viewed from the height at the origin converges to an invariant measure. In dimension three and higher, the process itself converges to an invariant measure in L^2 at speed t^{1-d/2} (this extends the convergence established by Hsiao). When the noise is Gaussian the limiting measures are Gaussian fields (harmonic crystals) and are also reversible for the process.

math.PR↗

Multiclass processes, dual points and M/M/1 queues

We consider the discrete Hammersley-Aldous-Diaconis process (HAD) and the totally asymmetric simple exclusion process (TASEP) in Z. The basic coupling induces a multiclass process which is useful in discussing shock measures and other important properties of the processes. The invariant measures of the multiclass systems are the same for both processes, and can be constructed as the law of the output process of a system of multiclass queues in tandem; the arrival and service processes of the queueing system are a collection of independent Bernoulli product measures. The proof of invariance involves a new coupling between stationary versions of the processes called a multi-line process; this process has a collection of independent Bernoulli product measures as an invariant measure. Some of these results have appeared elsewhere and this paper is partly a review, with some proofs given only in outline. However we emphasize a new approach via dual points: when the graphical construction is used to construct a trajectory of the TASEP or HAD process as a function of a Poisson process in ZxR, the dual points are those which govern the time-reversal of the trajectory. Each line of the multi-line process is governed by the dual points of the line below. We also mention some other processes whose multiclass versions have the same invariant measures, and we note an extension of Burke's theorem to multiclass queues which follows from the results.

math-ph↗

Competition interfaces and second class particles

The one-dimensional nearest-neighbor totally asymmetric simple exclusion process can be constructed in the same space as a last-passage percolation model in Z^2. We show that the trajectory of a second class particle in the exclusion process can be linearly mapped into the competition interface between two growing clusters in the last-passage percolation model. Using technology built up for geodesics in percolation, we show that the competition interface converges almost surely to an asymptotic random direction. As a consequence we get a new proof for the strong law of large numbers for the second class particle in the rarefaction fan and describe the distribution of the asymptotic angle of the competition interface.

math.PR↗

Spatial birth-and-death processes in random environment

We consider birth-and-death processes of objects (animals) defined in ${\bf Z}^d$ having unit death rates and random birth rates. For animals with uniformly bounded diameter we establish conditions on the rate distribution under which the following holds for almost all realizations of the birth rates: (i) the process is ergodic with at worst power-law time mixing; (ii) the unique invariant measure has exponential decay of (spatial) correlations; (iii) there exists a perfect-simulation algorithm for the invariant measure. The results are obtained by first dominating the process by a backwards oriented percolation model, and then using a multiscale analysis due to Klein to establish conditions for the absence of percolation.

math.PR↗

Perfect simulation for interacting point processes, loss networks and Ising models

We present a perfect simulation algorithm for measures that are absolutely continuous with respect to some Poisson process and can be obtained as invariant measures of birth-and-death processes. Examples include area- and perimeter-interacting point processes (with stochastic grains), invariant measures of loss networks, and the Ising contour and random cluster models. The algorithm does not involve couplings of the process with different initial conditions and it is not tied up to monotonicity requirements. Furthermore, it directly provides perfect samples of finite windows of the infinite-volume measure, subjected to time and space ``user-impatience bias''. The algorithm is based on a two-step procedure: (i) a perfect-simulation scheme for a (finite and random) relevant portion of a (space-time) marked Poisson processes (free birth-and-death process, free loss networks), and (ii) a ``cleaning'' algorithm that trims out this process according to the interaction rules of the target process. The first step involves the perfect generation of ``ancestors'' of a given object, that is of predecessors that may have an influence on the birth-rate under the target process. The second step, and hence the whole procedure, is feasible if these ``ancestors'' form a finite set with probability one. We present a sufficiency criteria for this condition, based on the absence of infinite clusters for an associated (backwards) oriented percolation model.

math.PR↗

Processes with Long Memory: Regenerative Construction and Perfect Simulation

We present a perfect simulation algorithm for stationary processes indexed by Z, with summable memory decay. Depending on the decay, we construct the process on finite or semi-infinite intervals, explicitly from an i.i.d. uniform sequence. Even though the process has infinite memory, its value at time 0 depends only on a finite, but random, number of these uniform variables. The algorithm is based on a recent regenerative construction of these measures by Ferrari, Maass, Mart{\'ı}nez and Ney. As applications, we discuss the perfect simulation of binary autoregressions and Markov chains on the unit interval.

math.PR↗

Regularity of quasi-stationary measures for simple exclusion in dimension d >= 5

We consider the symmetric simple exclusion process on Z^d, for d>= 5, and study the regularity of the quasi-stationary measures of the dynamics conditionned on not occupying the origin. For each ρ\in ]0,1[, we establish uniqueness of the density of quasi-stationary measures in L^2(d\nur), where \nur is the stationary measure of density ρ. This, in turn, permits us to obtain sharp estimates for P_{\nur}(τ>t), where τis the first time the origin is occupied.

math.PR↗

Reconstruction of Gray-scale Images

Reconstruction of images corrupted by noise is an important problem in Image Analysis. In the standard Bayesian approach the unknown original image is assumed to be a realization of a Markov random field on a finite two dimensional finite region. This image is degraded by some noise, which acts independently in each site of and has the same distribution on all sites. The reconstructed image is the maximum a posteriori (MAP) estimator. When the number of colors is bigger than two the standard a priori measure is the Potts model, but with this choice reconstruction algorithms need exponential growing time in the number of pixels. We propose a different a priori measure that allows to compute the MAP estimator in polinomial time. The algorithm binary decomposes the intensity of each color and reconstructs each component, reducing the problem to the computation of the two-color MAP estimator. The latter is done in polinomial time solving a min-cut max-flow problem in a binary graph as proposed by Greig, Porteous and Seheult (1989).

math.ST↗

Detection of spatial pattern through independence of thinned processes

Let N, N' and N'' be point processes such that N' is obtained from N by homogeneous independent thinning and N''= N- N'. We give a new elementary proof that N' and N'' are independent if and only if N is a Poisson point process. We present some applications of this result to test if a homogeneous point process is a Poisson point process.

math.PR↗

Convergence to the maximal invariant measure for a zero-range process with random rates

We consider a one-dimensional totally asymmetric nearest-neighbor zero-range process with site-dependent jump-rates - an environment. For each environment p we prove that the set of all invariant measures is the convex hull of a set of product measures with geometric marginals. As a consequence we show that for environments p satisfying certain asymptotic property, there are no invariant measures concentrating on configurations with critical density bigger than $ρ^*(p)$, a critical value. If $ρ^*(p)$ is finite we say that there is phase-transition on the density. In this case we prove that if the initial configuration has asymptotic density strictly above $ρ^*(p)$, then the process converges to the maximal invariant measure.

math.PR↗

A Mixture of the Exclusion Process and the Voter Model

We consider a one-dimensional nearest-neighbor interacting particle system, which is a mixture of the simple exclusion process and the voter model. The state space is taken to be the countable set of the configurations that have a finite number of particles to the right of the origin and a finite number of empty sites to the left of it. We obtain criteria for the ergodicity and some other properties of this system using the method of Lyapunov functions.

math.PR↗

Poisson approximation for large-contours in low-temperature Ising models

We consider the contour representation of the infinite volume Ising model at low temperature. Fix a subset V of Z^d, and a (large) N such that calling G_{N,V} the set of contours of length at least N intersecting V, there are in average one contour in G_{N,V} under the infinite volume "plus" measure. We find bounds on the total variation distance between the law of the contours of lenght at least N intersecting V under the "plus" measure and a Poisson process. The proof builds on the Chen-Stein method as presented by Arratia, Goldstein and Gordon. The control of the correlations is obtained through the loss-network space-time representation of contours due to Fernandez, Ferrari and Garcia.

math.PR↗