Sublinear variance in first-passage percolation for general distributions
classification
🧮 math.PR
keywords
continuousdistributionsfirst-passagepercolationsublinearvarianceabsolutelyapplies
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We prove that the variance of the passage time from the origin to a point x in first-passage percolation on Z^d is sublinear in the distance to x when d \geq 2, obeying the bound Cx/(log x), under minimal assumptions on the edge-weight distribution. The proof applies equally to absolutely continuous, discrete and singular continuous distributions and mixtures thereof, and requires only 2+log moments. The main result extends work of Benjamini-Kalai-Schramm and Benaim-Rossignol.
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