Metropolis-Hastings Algorithm with Delayed Acceptance and Rejection

Modified Metropolis-Hastings algorithm with Delayed Rejection

The development of an efficient MCMC strategy for sampling from complex distributions is a difficult task that needs to be solved for calculating small failure probabilities encountered in high-dimensional reliability analysis of engineering systems. Usually different variations of the Metropolis-Hastings algorithm (MH) are used. However, the standard MH algorithm does generally not work in hig...

On Metropolis-Hastings algorithms with delayed rejection

Modified Metropolis-Hastings algorithm with Delayed Rejection for High-Dimensional Reliability Analysis

The development of an efficient MCMC strategy for sampling from complex distributions is a difficult task that needs to be solved for calculating small failure probabilities encountered in high-dimensional reliability analysis of engineering systems. Usually different variations of the Metropolis-Hastings algorithm (MH) are used. However, the standard MH algorithm does generally not work in hig...

The Metropolis-Hastings-Green Algorithm

1.1 Dimension Changing The Metropolis-Hastings-Green algorithm (as opposed to just MetropolisHastings with no Green) is useful for simulating probability distributions that are a mixture of distributions having supports of different dimension. An early example (predating Green’s general formulation) was an MCMC algorithm for simulating spatial point processes (Geyer and Møller, 1994). More wide...

Understanding the Metropolis-Hastings Algorithm

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