  From its inception in the 1950s to the modern frontiers of applied
statistics, Markov chain Monte Carlo has been one of the most ubiquitous and
successful methods in statistical computing. In that time its development has
been fueled by increasingly difficult problems and novel techniques from
physics. In this article I will review the history of Markov chain Monte Carlo
from its inception with the Metropolis method to today's state-of-the-art in
Hamiltonian Monte Carlo. Along the way I will focus on the evolving interplay
between the statistical and physical perspectives of the method.
