Record-breaking statistics for random walks in the presence of measurement error and noise

Abstract

We address the question of distance record-setting by a random walker in the presence of measurement error, $\delta$, and additive noise, $\gamma$ and show that the mean number of (upper) records up to $n$ steps still grows universally as $<R_n>\sim n^{1/2}$ for large $n$ for all jump distributions, including Lévy flights, and for all $\delta$ and $\gamma$. In contrast to the universal growth exponent of 1/2, the pace of record setting, measured by the pre-factor of $n^{1/2}$, depends on $\delta$ and $\gamma$. In the absence of noise ($\gamma =0$), the pre-factor $S(\delta )$ is evaluated explicitly for arbitrary jump distributions and it decreases monotonically with increasing $\delta$ whereas, in case of perfect measurement $(\delta =0)$, the corresponding pre-factor $T(\gamma )$ increases with $\gamma$. Our analytical results are supported by extensive numerical simulations and qualitatively similar results are found in two and three dimensions.

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• PhD thesis "Extreme value statistics of strongly correlated systems: fermions, random matrices and random walks"