With the term Markov Chain Monte Carlo, notably referred as MCMC, we refer to statistical methods that allow to generate a dependent sample from a probability distribution. MCMC utilises a Monte Carlo method based upon Markov Chains, and its main application is primarily in Bayesian statistics to provide samples from the posterior distribution. MCMC can be traced back to Metropolis et al. (1953) and Hastings (1970), but it became very popular after the publication of the Gibbs Sampler by Geman and Geman (1984).

An important aspect of the applicability of this family of methods is that the likelihood function must…

Andrea Giussani

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