A Tutorial on Reversible Jump MCMC with a View toward Applications in QTL‐mapping

By Rasmus Waagepetersen, D. Sorensen. 2001

103 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ca1d396acf9cec6a77f907c2bbe9e90871710dd4

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Citations in the corpus

Norges Teknisk-naturvitenskapelige Universitet Control Variates for the Metropolis-hastings Algorithm Control Variates for the Metropolis-hastings Algorithm (unknown): “This short description is based on Waagepetersen and Sørensen (2001).”
Model choice using reversible jump Markov chain Monte Carlo (2012, background): “Of note is the tutorial by Waagepetersen and Sorensen (2001) which follows the lines of Green (1995) but in more detail and minimizing the measure theoretic notation.”
Bayesian Model Averaging in the Instrumental Variable Regression Model (2012, unknown): “A contribution of the present paper is to design a reversible jump Markov chain Monte Carlo algorithm (RJMCMC, see Green, 1995 or Waagepetersen and Sorensen, 2001) that explores the joint posterior distribution of parameters and models and thus allows us to do BMA.”
Bayesian analysis of complex traits in pedigreed plant populations (2008, methodology): “Treating the number of genes as a random variable in a Bayesian framework can be facilitated by the use of the Reversible Jump sampler (Green 1995; Waagepetersen and Sorensen 2001).”
A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces (2006, background): “As the Jacobian of the transformation implied by (2) is 1 in absolute value1, detailed balance also holds in terms of arbitrary measurable sets, as required for reversibility of the Markov chain (Waagepetersen and Sorensen, 2001).”