Abstract:Abstract In this article, we introduce the basic ideas of Markov chain Monte Carlo simulation. We start by briefly commenting on the main ideas of Monte Carlo sampling and then by reviewing the theory...Abstract In this article, we introduce the basic ideas of Markov chain Monte Carlo simulation. We start by briefly commenting on the main ideas of Monte Carlo sampling and then by reviewing the theory of Markov chains, concentrating particularly on the conditions required for the existence of a stationary distribution. We then introduce the Metropolis–Hastings algorithm and show that this generates a Markov chain with a specific stationary distribution. We next introduce some specific ways of implementing the Metropolis–Hastings algorithm, via the independence sampler, the Metropolis sampler, and the Gibbs sampler among others. We also comment on how the convergence of a Markov chain to equilibrium can be assessed in practice and provide an illustrating example. Finally, we review some of the freely available, existing software for implementing MCMC methods.Read More
Publication Year: 2015
Publication Date: 2015-09-16
Language: en
Type: other
Indexed In: ['crossref']
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Cited By Count: 2
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