Abstract
In this article, we study linearly edge-reinforced random walk on general multi-level ladders for large initial edge weights. For infinite ladders, we show that the process can be represented as a random walk in a random environment, given by random weights on the edges. The edge weights decay exponentially in space. The process converges to a stationary process. We provide asymptotic bounds for the range of the random walker up to a given time, showing that it localizes much more than an ordinary random walker. The random environment is described in terms of an infinite-volume Gibbs measure.
Original language | English |
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Pages (from-to) | 115-140 |
Number of pages | 26 |
Journal | Annals of Probability |
Volume | 35 |
Issue number | 1 |
DOIs | |
State | Published - 2007 |
Keywords
- Convergence to equilibrium
- Gibbs measure
- Random environment
- Reinforced random walk