Abstract
Abstract Practically relevant statistical models often give rise to probability distributions that are analytically intractable. Fortunately, we now have a collection of algorithms, known as Markov chain Monte Carlo (MCMC) , that has brought many of these models within our computational reach. MCMC is a simulation technique that allows us to make (approximate) draws from complex, high‐dimensional probability distributions. A staggering amount of research has been done on both the theoretical and applied aspects of MCMC. This article does not intend to be a complete overview of MCMC but only hopes to get the reader started in the right direction. To this end, this article begins with a general description of the types of problems that necessitate the use of MCMC. It then introduces the fundamental algorithms and addresses some general implementation issues.
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