Efficient Bayes factor estimation from the reversible jump output

By Francesco Bartolucci, Luisa Scaccia, Antonietta Mira. 2006

58 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/c3beb1622a48d7fe17a1b085a6aa86ea42eb3be2

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In Artificial Intelligence

In Control and Systems Engineering

Citations in the corpus

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): “Further manipulation is required to estimate Bk′,k if the sampler does not jump between models Mk and Mk′ directly (Bartolucci et al., 2006).”
Approximate Bayesian computational methods (2011, result): “This relates to earlier comments about the ABC acceptance rate approximating the marginal and a recent paper by Bartolucci et al. (2006) studying ways of computing marginal probabilities by Rao–Blackwellising reversible jump acceptance probabilities.”
Marginal likelihood estimation via power posteriors (2008, methodology): “This data set appears in Han and Carlin (2001), and was also examined in Carlin and Chib (1995) and Bartolucci et al. (2006), where several methods were compared to estimate the Bayes factor between two non-nested competing models.”
Properties of nested sampling (2008, unknown): “We first recover Gelfand and Dey’s (1994) solution of reverse importance sampling by an integrated reversible jump, because a natural approach to compute a marginal likelihood is to use a reversible jump Markov chain Monte Carlo algorithm (Green, 1995).”
Estimating the Integrated Likelihood via Posterior Simulation Using the Harmonic Mean Identity (2006, methodology): “Bayes factors can be read off the output of transdimensional MCMC directly, and more efficient approaches to estimating Bayes factors from transdimensional MCMC have been discussed by Bartolucci, Scaccia, and Mira (2006).”
Automatic Detection of Key Innovations, Rate Shifts, and Diversity-Dependence on Phylogenetic Trees (2014, unknown): “Because rjMCMC samples diversification models in proportion to their posterior probability [31,33], model selection emerges automatically from the analysis.”
Estimating the Marginal Likelihood Using the Arithmetic Mean Identity (2017, methodology): “We now present a classical example for Bayesian model selection considered among others in Carlin and Chib (1995), Han and Carlin (2001), Bartolucci et al. (2006), Friel and Pettitt (2008), where different methods for estimating the Bayes factor between two non-nested competing linear regression…”
Model choice using reversible jump Markov chain Monte Carlo (2012, background): “Bartolucci et al. (2006) extend Equation (6) to the trans-dimensional case and suggest a choice of the function that requires no extra computational cost.”
Towards Automatic Model Comparison: An Adaptive Sequential Monte Carlo Approach (2013, methodology): “A post-processing approach to improve the computation of normalising constants from RJMCMC output using a bridge-sampling approach was advocated by Bartolucci et al. (2006).”