2,908 research outputs found

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

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    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.Comment: Revised version after referee of SPA: 39 page

    Loss network representation of Peierls contours

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    We present a probabilistic approach for the study of systems with exclusions, in the regime traditionally studied via cluster-expansion methods. In this paper we focus on its application for the gases of Peierls contours found in the study of the Ising model at low temperatures, but most of the results are general. We realize the equilibrium measure as the invariant measure of a loss-network process whose existence is ensured by a subcriticality condition of a dominant branching process. In this regime, the approach yields, besides existence and uniqueness of the measure, properties such as exponential space convergence and mixing, and a central limit theorem. The loss network converges exponentially fast to the equilibrium measure, without metastable traps. This convergence is faster at low temperatures, where it leads to the proof of an asymptotic Poisson distribution of contours. Our results on the mixing properties of the measure are comparable to those obtained with ``duplicated-variables expansion'', used to treat systems with disorder and coupled map lattices. It works in a larger region of validity than usual cluster-expansion formalisms, and it is not tied to the analyticity of the pressure. In fact, it does not lead to any kind of expansion for the latter, and the properties of the equilibrium measure are obtained without resorting to combinatorial or complex analysis techniques.Comment: 42 pages. Revised version after the first referee repor

    Developement of integrated procedures for survey, analisys and monitoring of historical buildings and antique monuments

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    Peer Reviewe

    Processes with Long Memory: Regenerative Construction and Perfect Simulation

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    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{\'\i}nez and Ney. As applications, we discuss the perfect simulation of binary autoregressions and Markov chains on the unit interval.Comment: 27 pages, one figure. Version accepted by Annals of Applied Probability. Small changes with respect to version

    Bradycardia and atrial fibrillation in patients with stable coronary artery disease treated with ivabradine: an analysis from the SIGNIFY study

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    Aim: The aim of this study was to determine the impact of emergent bradycardia and atrial fibrillation (AF) on cardiovascular outcomes in 19 083 patients with stable coronary artery disease (CAD) receiving ivabradine or placebo (SIGNIFY, Study assessInG the morbidity–mortality beNefits of the If inhibitor ivabradine in patients with coronarY artery disease). Methods and results: Emergent bradycardia (resting heart rate <50 b.p.m. on 12-lead electrocardiogram) with ivabradine was reported in 3572 patients (37.4%) overall, and in 2242 (37.2%) patients with Canadian Cardiovascular Society (CCS) class ≥2 angina. There was no difference in outcomes over the course of the study in ivabradine-treated patients with and without emergent bradycardia in the whole population (2.5 vs. 2.9% per year, respectively, for primary composite endpoint of cardiovascular death or non-fatal myocardial infarction) or in the angina subgroup (2.5 vs. 3.2% per year). Neither was there an increase in the rate of primary endpoint after emergent bradycardia was recorded compared with those without emergent bradycardia. There were 754 cases of emergent AF on treatment (2.2% per year ivabradine vs. 1.5% per year placebo) and 469 in the patients with angina (2.2 vs. 1.5% per year). While outcomes occurred more frequently in patients in whom emergent AF had been recorded, there was no treatment–placebo difference in outcomes, including stroke, and no difference in treatment effect in patients with limiting angina. Conclusion: Both in the overall population as well as in the angina subset, bradycardia was common in ivabradine-treated patients, but did not appear to impact outcomes. Emergent AF was relatively rare and did not appear to have an impact on outcomes relative to placebo
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