  The local pivotal method (LPM) is a successful sampling method for taking
well-spread samples from discrete populations. We show how the LPM can be
utilized to sample from arbitrary continuous distributions and thereby give
powerful variance reduction in general cases. The method creates an ``automatic
stratification" on any continuous distribution, of any dimension, and selects a
``thin" well-spread sample. We demonstrate the simplicity, generality and
effectiveness of the LPM with various examples, including Monte Carlo
estimation of integrals, option pricing and stability estimation in non-linear
dynamical systems. Additionally, we show how the LPM can be combined with other
variance reduction techniques, such as importance sampling, to achieve even
greater variance reduction. To facilitate the implementation of the LPM, we
provide a quick start guide to using LPM in MATLAB and R, which includes sample
code demonstrating how to achieve variance reduction with just a few lines of
code.
