3,871 research outputs found

    Oppen's Pragmatism

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    Ising models on locally tree-like graphs

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    We consider ferromagnetic Ising models on graphs that converge locally to trees. Examples include random regular graphs with bounded degree and uniformly random graphs with bounded average degree. We prove that the "cavity" prediction for the limiting free energy per spin is correct for any positive temperature and external field. Further, local marginals can be approximated by iterating a set of mean field (cavity) equations. Both results are achieved by proving the local convergence of the Boltzmann distribution on the original graph to the Boltzmann distribution on the appropriate infinite random tree.Comment: Published in at http://dx.doi.org/10.1214/09-AAP627 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

    Central limit theorem for biased random walk on multi-type Galton-Watson trees

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    Let T be a rooted supercritical multi-type Galton-Watson (MGW) tree with types coming from a finite alphabet, conditioned to non-extinction. The lambda-biased random walk (X_t, t>=0) on T is the nearest-neighbor random walk which, when at a vertex v with d(v) offspring, moves closer to the root with probability lambda/[lambda+d(v)], and to each of the offspring with probability 1/[lambda+d(v)]. This walk is recurrent for lambda>=rho and transient for 0<lambda<rho, with rho the Perron-Frobenius eigenvalue for the (assumed) irreducible matrix of expected offspring numbers. Subject to finite moments of order p>4 for the offspring distributions, we prove the following quenched CLT for lambda-biased random walk at the critical value lambda=rho: for almost every T, the process |X_{floor(nt)}|/sqrt{n} converges in law as n tends to infinity to a reflected Brownian motion rescaled by an explicit constant. This result was proved under some stronger assumptions by Peres-Zeitouni (2008) for single-type Galton-Watson trees. Following their approach, our proof is based on a new explicit description of a reversing measure for the walk from the point of view of the particle (generalizing the measure constructed in the single-type setting by Peres-Zeitouni), and the construction of appropriate harmonic coordinates. In carrying out this program we prove moment and conductance estimates for MGW trees, which may be of independent interest. In addition, we extend our construction of the reversing measure to a biased random walk with random environment (RWRE) on MGW trees, again at a critical value of the bias. We compare this result against a transience-recurrence criterion for the RWRE generalizing a result of Faraud (2011) for Galton-Watson trees.Comment: 44 pages, 1 figur

    No zero-crossings for random polynomials and the heat equation

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    Consider random polynomial βˆ‘i=0naixi\sum_{i=0}^na_ix^i of independent mean-zero normal coefficients aia_i, whose variance is a regularly varying function (in ii) of order Ξ±\alpha. We derive general criteria for continuity of persistence exponents for centered Gaussian processes, and use these to show that such polynomial has no roots in [0,1][0,1] with probability nβˆ’bΞ±+o(1)n^{-b_{\alpha}+o(1)}, and no roots in (1,∞)(1,\infty) with probability nβˆ’b0+o(1)n^{-b_0+o(1)}, hence for nn even, it has no real roots with probability nβˆ’2bΞ±βˆ’2b0+o(1)n^{-2b_{\alpha}-2b_0+o(1)}. Here, bΞ±=0b_{\alpha}=0 when Ξ±β‰€βˆ’1\alpha\le-1 and otherwise bα∈(0,∞)b_{\alpha}\in(0,\infty) is independent of the detailed regularly varying variance function and corresponds to persistence probabilities for an explicit stationary Gaussian process of smooth sample path. Further, making precise the solution Ο•d(x,t)\phi_d({\mathbf{x}},t) to the dd-dimensional heat equation initiated by a Gaussian white noise Ο•d(x,0)\phi_d({\mathbf{x}},0), we confirm that the probability of Ο•d(x,t)β‰ 0\phi_d({\mathbf{x}},t)\neq0 for all t∈[1,T]t\in[1,T], is Tβˆ’bΞ±+o(1)T^{-b_{\alpha}+o(1)}, for Ξ±=d/2βˆ’1\alpha=d/2-1.Comment: Published in at http://dx.doi.org/10.1214/13-AOP852 the Annals of Probability (http://www.imstat.org/aop/) by the Institute of Mathematical Statistics (http://www.imstat.org
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