No Arabic abstract
We prove a Large Deviations Principle for the number of intersections of two independent infinite-time ranges in dimension five and more, improving upon the moment bounds of Khanin, Mazel, Shlosman and Sina{i} [KMSS94]. This settles, in the discrete setting, a conjecture of van den Berg, Bolthausen and den Hollander [BBH04], who analyzed this question for the Wiener sausage in finite-time horizon. The proof builds on their result (which was resumed in the discrete setting by Phetpradap [Phet12]), and combines it with a series of tools that were developed in recent works of the authors [AS17, AS19a, AS20]. Moreover, we show that most of the intersection occurs in a single box where both walks realize an occupation density of order one.
We prove that the Beta random walk has second order cubic fluctuations from the large deviation principle of the GUE Tracy-Widom type for arbitrary values $upalpha>0$ and $upbeta>0$ of the parameters of the Beta distribution, removing previous restrictions on their values. Furthermore, we prove that the GUE Tracy-Widom fluctuations still hold in the intermediate disorder regime. We also show that any random walk in space-time random environment that matches certain moments with the Beta random walk also has GUE Tracy-Widom fluctuations in the intermediate disorder regime. As a corollary we show the emergence of GUE Tracy-Widom fluctuations from the large deviation principle for trajectories ending at boundary points for random walks in space (time-independent) i.i.d. Dirichlet random environment in dimension $d=2$ for a class of asymptotic behavior of the parameters.
We study one-dimensional nearest neighbour random walk in site-random environment. We establish precise (sharp) large deviations in the so-called ballistic regime, when the random walk drifts to the right with linear speed. In the sub-ballistic regime, when the speed is sublinear, we describe the precise probability of slowdown.
Let ${{bf mathcal{Z}}_n:ngeq 1}$ be a sequence of i.i.d. random probability measures. Independently, for each $ngeq 1$, let $(X_{n1},ldots, X_{nn})$ be a random vector of positive random variables that add up to one. This paper studies the large deviation principles for the randomly weighted sum $sum_{i=1}^{n} X_{ni} mathcal{Z}_i$. In the case of finite Dirichlet weighted sum of Dirac measures, we obtain an explicit form for the rate function. It provides a new measurement of divergence between probabilities. As applications, we obtain the large deviation principles for a class of randomly weighted means including the Dirichlet mean and the corresponding posterior mean. We also identify the minima of relative entropy with mean constraint in both forward and reverse directions.
Let $sigma(u)$, $uin mathbb{R}$ be an ergodic stationary Markov chain, taking a finite number of values $a_1,...,a_m$, and $b(u)=g(sigma(u))$, where $g$ is a bounded and measurable function. We consider the diffusion type process $$ dX^epsilon_t = b(X^epsilon_t/epsilon)dt + epsilon^kappasigmabig(X^epsilon_t/epsilonbig)dB_t, tle T $$ subject to $X^epsilon_0=x_0$, where $epsilon$ is a small positive parameter, $B_t$ is a Brownian motion, independent of $sigma$, and $kappa> 0$ is a fixed constant. We show that for $kappa<1/6$, the family ${X^epsilon_t}_{epsilonto 0}$ satisfies the Large Deviations Principle (LDP) of the Freidlin-Wentzell type with the constant drift $mathbf{b}$ and the diffusion $mathbf{a}$, given by $$ mathbf{b}=sumlimits_{i=1}^mdfrac{g(a_i)}{a^2_i}pi_iBig/ sumlimits_{i=1}^mdfrac{1}{a^2_i}pi_i, quad mathbf{a}=1Big/sumlimits_{i=1}^mdfrac{1}{a^2_i}pi_i, $$ where ${pi_1,...,pi_m}$ is the invariant distribution of the chain $sigma(u)$.
We consider a variant of a classical coverage process, the boolean model in $mathbb{R}^d$. Previous efforts have focused on convergence of the unoccupied region containing the origin to a well studied limit $C$. We study the intersection of sets centered at points of a Poisson point process confined to the unit ball. Using a coupling between the intersection model and the original boolean model, we show that the scaled intersection converges weakly to the same limit $C$. Along the way, we present some tools for studying statistics of a class of intersection models.