Delayed rejection in reversible jump Metropolis–Hastings

By Peter J. Green, Antonietta Mira. 2001

309 citations recorded by the source corpus.

Subfields: Mathematical Physics, Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/03d9a571ca66e995ff742ceaf75f51c5bb9418f1

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Citations in the corpus

Bayesian calibration of a large‐scale geothermal reservoir model by a new adaptive delayed acceptance Metropolis Hastings algorithm (2011, background): “The notations and 0 indicate that different functions could be used in each direction though these functions might often be the same in practice [Green and Mira, 2001].”
Handbook of Markov Chain Monte Carlo: Hardcover: 619 pages Publisher: Chapman and Hall/CRC Press (first edition, May 2011) Language: English ISBN-10: 1420079417 (2012, background): “Green and Mira (2001) introduce a procedure for learning from rejected between-model proposals based on an extension of the splitting rejection idea of Tierney and Mira (1999).”
Simulation-based optimal Bayesian experimental design for nonlinear systems (2011, methodology): “Two useful improvements to MH are the concepts of delayed rejection (DR) [77, 78] and adaptive Metropolis (AM) [79]; combining these lead to the DRAM algorithm of Haario et al.”
Markov chain Monte Carlo Using an Approximation (2005, background): “The primary aim of delayed rejection is to increase statistical efﬁciency, though some examples showed a rather small improvement in computational efﬁciency (Green and Mira 2001).”
Model choice using reversible jump Markov chain Monte Carlo (2012, methodology): “An interesting modification to Metropolis–Hastings is the splitting rejection idea of Tierney and Mira (1999), extended to the reversible jump setting by Green and Mira (2001), who call it delayed rejection.”
Efficient construction of reversible jump Markov chain Monte Carlo proposal distributions (2003, methodology): “As a direct extension of the reversible jump, Green and Mira (2001) introduced a procedure for reassigning rejected moves by sequentially attempting further proposals using a modification of the usual accept–reject mechanism.”
Improving the acceptance rate of reversible jump MCMC proposals (2004, background): “Suggestions have been made for improving acceptance rates in RJMCMC. Green and Mira (2001) introduce a second stage to the proposal mechanism in their “delayed rejection sampler”.”
Survey of multifidelity methods in uncertainty propagation, inference, and optimization (2018, methodology): “Multistage MCMC methods based on delayed rejection, in contrast to delayed acceptance, have been proposed in [200, 144, 85].”
Zero variance Markov chain Monte Carlo for Bayesian estimators (2010, background): “These modifications have been achieved in many different ways, for example by trying to induce negative correlation along the chain path ([6,21,13,41,12]); by trying to avoid random walk behavior via successive over-relaxation ([1,36,7]); by hybrid Monte Carlo ([16,35,10,18,27]); by exploiting non r”
Seismic tomography with the reversible jump algorithm (2009, methodology): “An issue for future study would be to extend this scheme to transdimensional moves as shown in Green & Mira (2001) or to use recent development of assisted or automated proposal generation for transdimensional sampling scheme (e.g. Tierney & Mira 1999; Haario et al. 2001; Al-Awadhi et al. 2004;…”
Markov chain Monte Carlo (MCMC) sampling methods to determine optimal models, model resolution and model choice for Earth Science problems (2009, methodology): “A variant of the basic algorithm known as delayed rejection (e.g. Green and Mira, 2001; Al- Awadhi et al., 2002), can be used to improve the acceptance rate by modifying the proposal function during an iteration.”