A Central Limit Theorem for biased random walks on Galton-Watson trees
Yuval Peres, Ofer Zeitouni
Abstract
Let T be a rooted Galton-Watson tree with offspring distribution \pk\ that has p0=0, mean m=Σ kpk>1 and exponential tails. Consider the λ-biased random walk \Xn\n≥ 0 on T; this is the nearest neighbor random walk which, when at a vertex v with dv offspring, moves closer to the root with probability λ/(λ+dv), and moves to each of the offspring with probability 1/(λ+dv). It is known that this walk has an a.s. constant speed =n |Xn|/n (where |Xn| is the distance of Xn from the root), with >0 for 0<λ<m and =0 for λ m. For all λ m, we prove a quenched CLT for |Xn|-n. (For λ>m the walk is positive recurrent, and there is no CLT.) The most interesting case by far is λ=m, where the CLT has the following form: for almost every T, the ratio |X[nt]|/n converges in law as n ∞ to a deterministic multiple of the absolute value of a Brownian motion. Our approach to this case is based on an explicit description of an invariant measure for the walk from the point of view of the particle (previously, such a measure was explicitly known only for λ=1) and the construction of appropriate harmonic coordinates.
Create a lesson
Related papers
Distribution-constrained optimal multiple stopping: the Root-type solution
Shuoqing Deng, Daxin Huang
Universality and sharp thresholds for ellipsoid fitting
Frederic Koehler, Youngtak Sohn
Local Laws and Edge Universality for Noncentral Sample Covariance Matrices
Can Hu, Jiang Hu, Zhidong Bai
Well-posedness and regularity of stochastic heat equations on moving domains
Chongyang Ren, Tusheng Zhang
Traveling Waves in Equity Markets with Rank-Based Entry and Exit
Graeme Baker, Caroline Smyth
An approximate zero bias transformation for random sums: Applications to sampling with outliers, auto insurance, and generative AI
Wasamon Jantai, Nathakhun Wiroonsri