Simulation-Based Bayesian Analysis

I consider the development of Markov chain Monte Carlo (MCMC) methods, from late-1980s Gibbs sampling to present-day gradient-based methods and piecewise-deterministic processes. In parallel, show how these ideas have been implemented in successive generations statistical software for Bayesian inference. These packages instrumental popularizing applied modeling across a wide variety scientific domains. They provide an invaluable service statisticians hiding complexities MCMC user while providing convenient language tools summarize output model. As research into new remains very active, it is likely that future will incorporate improve experience.