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Functional limit theorems for the multi-dimensional elephant random walk

2021; Taylor & Francis; Volume: 38; Issue: 1 Linguagem: Inglês

10.1080/15326349.2021.1971092

1532-4214

Marco Bertenghi,

Markov Chains and Monte Carlo Methods

In this article we shall derive functional limit theorems for the multi-dimensional elephant random walk (MERW) and thus extend the results provided for the one-dimensional marginal by Bercu and Laulin. The MERW is a non-Markovian discrete-time random walk on Zd which has a complete memory of its whole past, in allusion to the traditional saying that an elephant never forgets. As the name suggests, the MERW is a d-dimensional generalization of the elephant random walk (ERW), the latter was first introduced by Schütz and Trimper in 2004. We measure the influence of the elephant’s memory by a so-called memory parameter p between zero and one. A striking feature that has been observed in Schütz and Trimper is that the long-time behavior of the ERW exhibits a phase transition at some critical memory parameter pc. We investigate the asymptotic behavior of the MERW in all memory regimes by exploiting a connection between the MERW and Pólya urns, following similar ideas as in the work by Baur and Bertoin for the ERW.