2017
DOI: 10.1214/15-aop1071
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Invariance principle for variable speed random walks on trees

Abstract: We consider stochastic processes on complete, locally compact tree-like metric spaces $(T,r)$ on their "natural scale" with boundedly finite speed measure $\nu$. Given a triple $(T,r,\nu)$ such a speed-$\nu$ motion on $(T,r)$ can be characterized as the unique strong Markov process which if restricted to compact subtrees satisfies for all $x,y\in T$ and all positive, bounded measurable $f$, \[ \mathbb{E}^x [ \int^{\tau_y}_0\mathrm{d}s\, f(X_s) ] = 2\int_T\nu(\mathrm{d}z)\, r(y,c(x,y,z))f(z) < \infty, \] wh… Show more

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Cited by 43 publications
(50 citation statements)
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“…Under certain assumptions, that will be verified in our context (see (8) in [49] for details), the process . T .…”
Section: Spatial Treesmentioning
confidence: 83%
See 1 more Smart Citation
“…Under certain assumptions, that will be verified in our context (see (8) in [49] for details), the process . T .…”
Section: Spatial Treesmentioning
confidence: 83%
“…It was recently shown by Croydon, Hambly, and Kumagai in [27] and by Croydon in [26] that the convergence of measured resistance networks imply the convergence of the corresponding processes (see [8] for a related invariance principle). Furthermore, the convergence of spatial measured networks imply the convergence of the corresponding embedded processes (theorem 7.1 in [26]).…”
Section: Resistance Network and Convergence Of Processesmentioning
confidence: 98%
“…Loosely speaking, the embedding works as follows. We first choose a N (m) (recall that m is the starting point in (2)) and a stopping time ρ 1 such that E(ρ 1 ) = 1/N and M ρ 1 d = Y N 1 . Conditionally on {M ρ 1 = y} we choose a N (y) and a stopping time ρ 2 such that E(ρ 2 ) = 1/N and M ρ 1 +ρ 2 d = y + a N (y)X 2 .…”
Section: Introductionmentioning
confidence: 99%
“…Our approach to generalize Donsker's theorem is essentially different from the one pioneered by Stone in [12] (also see [2] for a recent generalization to tree-valued processes). In that approach the approximating processes are continuous-time Markov processes that do not jump over points in their state spaces (that is, they can be e.g.…”
Section: Introductionmentioning
confidence: 99%
“…But they also appear in very recent research as speed measures of Brownian motions on Ê-trees [AEW13,ALW15] or sampling measures for spatial Fleming-Viot processes [GSW15]. Convergence of metric measure spaces with boundedly finite measures has been analysed with a view towards probabilistic applications in [ALW16].…”
Section: Introductionmentioning
confidence: 99%