Probability Theory and Related Fields最新文献

We study the mixing time of a random walker who moves inside a dynamical random cluster model on the d-dimensional torus of side-length n. In this model, edges switch at rate (mu ) between open and closed, following a Glauber dynamics for the random cluster model with parameters p, q. At the same time, the walker jumps at rate 1 as a simple random walk on the torus, but is only allowed to traverse open edges. We show that for small enough p the mixing time of the random walker is of order (n^2/mu ). In our proof we construct a non-Markovian coupling through a multi-scale analysis of the environment, which we believe could be more widely applicable.

In Bayesian statistics, posterior contraction rates (PCRs) quantify the speed at which the posterior distribution concentrates on arbitrarily small neighborhoods of a true model, in a suitable way, as the sample size goes to infinity. In this paper, we develop a new approach to PCRs, with respect to strong norm distances on parameter spaces of functions. Critical to our approach is the combination of a local Lipschitz-continuity for the posterior distribution with a dynamic formulation of the Wasserstein distance, which allows to set forth an interesting connection between PCRs and some classical problems arising in mathematical analysis, probability and statistics, e.g., Laplace methods for approximating integrals, Sanov’s large deviation principles in the Wasserstein distance, rates of convergence of mean Glivenko–Cantelli theorems, and estimates of weighted Poincaré–Wirtinger constants. We first present a theorem on PCRs for a model in the regular infinite-dimensional exponential family, which exploits sufficient statistics of the model, and then extend such a theorem to a general dominated model. These results rely on the development of novel techniques to evaluate Laplace integrals and weighted Poincaré–Wirtinger constants in infinite-dimension, which are of independent interest. The proposed approach is applied to the regular parametric model, the multinomial model, the finite-dimensional and the infinite-dimensional logistic-Gaussian model and the infinite-dimensional linear regression. In general, our approach leads to optimal PCRs in finite-dimensional models, whereas for infinite-dimensional models it is shown explicitly how the prior distribution affect PCRs.

A well-known open problem on the behavior of optimal paths in random graphs in the strong disorder regime, formulated by statistical physicists, and supported by a large amount of numerical evidence over the last decade (Braunstein et al. in Phys Rev Lett 91(16):168701, 2003; Braunstein et al. in Int J Bifurc Chaos 17(07):2215–2255, 2007; Chen et al. in Phys Rev Lett 96(6):068702, 2006; Wu et al. in Phys Rev Lett 96(14):148702, 2006) is as follows: for a large class of random graph models with degree exponent (tau in (3,4)), distances in the minimal spanning tree (MST) on the giant component in the supercritical regime scale like (n^{(tau -3)/(tau -1)}). The aim of this paper is to make progress towards a proof of this conjecture. We consider a supercritical inhomogeneous random graph model with degree exponent (tau in (3, 4)) that is closely related to Aldous’s multiplicative coalescent, and show that the MST constructed by assigning i.i.d. continuous weights to the edges in its giant component, endowed with the tree distance scaled by (n^{-(tau -3)/(tau -1)}), converges in distribution with respect to the Gromov–Hausdorff topology to a random compact real tree. Further, almost surely, every point in this limiting space either has degree one (leaf), or two, or infinity (hub), both the set of leaves and the set of hubs are dense in this space, and the Minkowski dimension of this space equals ((tau -1)/(tau -3)). The multiplicative coalescent, in an asymptotic sense, describes the evolution of the component sizes of various near-critical random graph processes. We expect the limiting spaces in this paper to be the candidates for the scaling limit of the MST constructed for a wide array of other heavy-tailed random graph models.

In this paper we introduce the critical variational setting for parabolic stochastic evolution equations of quasi- or semi-linear type. Our results improve many of the abstract results in the classical variational setting. In particular, we are able to replace the usual weak or local monotonicity condition by a more flexible local Lipschitz condition. Moreover, the usual growth conditions on the multiplicative noise are weakened considerably. Our new setting provides general conditions under which local and global existence and uniqueness hold. In addition, we prove continuous dependence on the initial data. We show that many classical SPDEs, which could not be covered by the classical variational setting, do fit in the critical variational setting. In particular, this is the case for the Cahn–Hilliard equation, tamed Navier–Stokes equations, and Allen–Cahn equation.

In the Fastest Mixing Markov Chain problem, we are given a graph (G = (V, E)) and desire the discrete-time Markov chain with smallest mixing time (tau ) subject to having equilibrium distribution uniform on V and non-zero transition probabilities only across edges of the graph. It is well-known that the mixing time (tau _textsf {RW}) of the lazy random walk on G is characterised by the edge conductance (Phi ) of G via Cheeger’s inequality: (Phi ^{-1} lesssim tau _textsf {RW} lesssim Phi ^{-2} log |V|). Analogously, we characterise the fastest mixing time (tau ^star ) via a Cheeger-type inequality but for a different geometric quantity, namely the vertex conductance (Psi ) of G: (Psi ^{-1} lesssim tau ^star lesssim Psi ^{-2} (log |V|)^2). This characterisation forbids fast mixing for graphs with small vertex conductance. To bypass this fundamental barrier, we consider Markov chains on G with equilibrium distribution which need not be uniform, but rather only (varepsilon )-close to uniform in total variation. We show that it is always possible to construct such a chain with mixing time (tau lesssim varepsilon ^{-1} ({text {diam}} G)^2 log |V|). Finally, we discuss analogous questions for continuous-time and time-inhomogeneous chains.

In the negative perceptron problem we are given n data points ((varvec{x}_i,y_i)), where (varvec{x}_i) is a d-dimensional vector and (y_iin {+1,-1}) is a binary label. The data are not linearly separable and hence we content ourselves to find a linear classifier with the largest possible negative margin. In other words, we want to find a unit norm vector (varvec{theta }) that maximizes (min _{ile n}y_ilangle varvec{theta },varvec{x}_irangle ). This is a non-convex optimization problem (it is equivalent to finding a maximum norm vector in a polytope), and we study its typical properties under two random models for the data. We consider the proportional asymptotics in which (n,drightarrow infty ) with (n/drightarrow delta ), and prove upper and lower bounds on the maximum margin (kappa _{{textrm{s}}}(delta )) or—equivalently—on its inverse function (delta _{{textrm{s}}}(kappa )). In other words, (delta _{{textrm{s}}}(kappa )) is the overparametrization threshold: for (n/dle delta _{{textrm{s}}}(kappa )-{varepsilon }) a classifier achieving vanishing training error exists with high probability, while for (n/dge delta _{{textrm{s}}}(kappa )+{varepsilon }) it does not. Our bounds on (delta _{{textrm{s}}}(kappa )) match to the leading order as (kappa rightarrow -infty ). We then analyze a linear programming algorithm to find a solution, and characterize the corresponding threshold (delta _{textrm{lin}}(kappa )). We observe a gap between the interpolation threshold (delta _{{textrm{s}}}(kappa )) and the linear programming threshold (delta _{textrm{lin}}(kappa )), raising the question of the behavior of other algorithms.

We prove the Perelman type W-entropy formula for the geodesic flow on the (L^2)-Wasserstein space over a complete Riemannian manifold equipped with Otto’s infinite dimensional Riemannian metric. To better understand the similarity between the W-entropy formula for the geodesic flow on the Wasserstein space and the W-entropy formula for the heat flow of the Witten Laplacian on the underlying manifold, we introduce the Langevin deformation of flows on the Wasserstein space over a Riemannian manifold, which interpolates the gradient flow and the geodesic flow on the Wasserstein space over a Riemannian manifold, and can be regarded as the potential flow of the compressible Euler equation with damping on a Riemannian manifold. We prove the existence, uniqueness and regularity of the Langevin deformation on the Wasserstein space over the Euclidean space and a compact Riemannian manifold, and prove the convergence of the Langevin deformation for (crightarrow 0) and (crightarrow infty ) respectively. Moreover, we prove the W-entropy-information formula along the Langevin deformation on the Wasserstein space on Riemannian manifolds. The rigidity theorems are proved for the W-entropy for the geodesic flow and the Langevin deformation on the Wasserstein space over complete Riemannian manifolds with the CD(0, m)-condition. Our results are new even in the case of Euclidean spaces and complete Riemannian manifolds with non-negative Ricci curvature.

We consider the problem of finding the initial vertex (Adam) in a Barabási–Albert tree process ( (mathcal {T}(n): n ge 1)) at large times. More precisely, given ( varepsilon >0), one wants to output a subset ( mathcal {P}_{ varepsilon }(n)) of vertices of ( mathcal {T}(n)) so that the initial vertex belongs to ( mathcal {P}_ varepsilon (n)) with probability at least (1- varepsilon ) when n is large. It has been shown by Bubeck, Devroye and Lugosi, refined later by Banerjee and Huang, that one needs to output at least ( varepsilon ^{-1 + o(1)}) and at most (varepsilon ^{-2 + o(1)}) vertices to succeed. We prove that the exponent in the lower bound is sharp and the key idea is that Adam is either a “large degree" vertex or is a neighbor of a “large degree" vertex (Eve).

We consider the standard first passage percolation model on ({mathbb {Z}}^ d) with a distribution G taking two values (0<a<b). We study the maximal flow through the cylinder ([0,n]^ {d-1}times [0,hn]) between its top and bottom as well as its associated minimal surface(s). We prove that the variance of the maximal flow is superconcentrated, i.e. in (O(frac{n^{d-1}}{log n})), for (hge h_0) (for a large enough constant (h_0=h_0(a,b))). Equivalently, we obtain that the ground state energy of a disordered Ising ferromagnet in a cylinder ([0,n]^ {d-1}times [0,hn]) is superconcentrated when opposite boundary conditions are applied at the top and bottom faces and for a large enough constant (hge h_0) (which depends on the law of the coupling constants). Our proof is inspired by the proof of Benjamini–Kalai–Schramm (Ann Probab 31:1970–1978, 2003). Yet, one major difficulty in this setting is to control the influence of the edges since the averaging trick used in Benjamini et al. (Ann Probab 31:1970–1978, 2003) fails for surfaces. Of independent interest, we prove that minimal surfaces (in the present discrete setting) cannot have long thin chimneys.

We compute the Wiener chaos decomposition of the signature for a class of Gaussian processes, which contains fractional Brownian motion (fBm) with Hurst parameter (H in (1/4,1)). At level 0, our result yields an expression for the expected signature of such processes, which determines their law (Chevyrev and Lyons in Ann Probab 44(6):4049–4082, 2016). In particular, this formula simultaneously extends both the one for (1/2 < H)-fBm (Baudoin and Coutin in Stochast Process Appl 117(5):550–574, 2007) and the one for Brownian motion ((H = 1/2)) (Fawcett 2003), to the general case (H > 1/4), thereby resolving an established open problem. Other processes studied include continuous and centred Gaussian semimartingales.