An educational/teaching tool for demonstrating the basic principles of the Markov chain Monte Carlo (MCMC) method used for numerical integration of probability distributions. Allows user to create one or more bivariate normal "hills" in a two dimensional field and start one to four "robots" walking on this surface. The steps taken by each robot represent a single Markov chain, and large samples of steps illustrate how MCMC simulation can approximate a probability density surface. If the case of multiple robots, one represent the "cold" chain while the other "heated" chains illustrate improvements in mixing resulting from swapping of chains (Metropolis-coupled MCMC).