Markov Chain Monte Carlo sampling

This is the third part in a short series of blog posts about quantum Monte Carlo (QMC). The series is derived from an introductory lecture I gave on the subject at the University of Guelph. Part 2 – Galton's peg board and the central limit theorem So far in this series we have seen various examples of random sampling. Here we'll look at a simple Python script that uses Markov chains and the Metropolis algorithm to randomly sample complicated two-dimensional probability distributions. If you come from a math, statistics, or physics background you may have leaned that a Markov chain is a set of states that are sampled from a probability distribution.