Random walks with non-Gaussian step-size distributions and the folding of random polymer chains

In this paper, we study a random walker whose step-size distribution is of non-Gaussian bimodal form due to the addition of a quartic term in the exponential. By the central limit theorem, we know that in the limit of a large number of steps, the probability distribution representing the distance the walker has traveled becomes Gaussian. We investigate the nature of this convergence both numerically and analytically. We obtain a scaling relation describing the number of steps required for convergence in terms of the width and separation of the peaks of the step-size distribution. We assume in the concluding section that our model is well suited for the application of the folding of a random polymer chain.