  This paper takes the reader on a journey through the history of Bayesian
computation, from the 18th century to the present day. Beginning with the
one-dimensional integral first confronted by Bayes in 1763, we highlight the
key contributions of: Laplace, Metropolis (and, importantly, his co-authors!),
Hammersley and Handscomb, and Hastings, all of which set the foundations for
the computational revolution in the late 20th century -- led, primarily, by
Markov chain Monte Carlo (MCMC) algorithms. A very short outline of 21st
century computational methods -- including pseudo-marginal MCMC, Hamiltonian
Monte Carlo, sequential Monte Carlo, and the various `approximate' methods --
completes the paper.
