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MCMC for Model Comparison/Ushahidi
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MCMC for Model Comparison

MCMC for model comparison uses Markov chain Monte Carlo algorithms to estimate the marginal likelihoods and Bayes factors needed to formally compare competing statistical models. Techniques such as reversible-jump MCMC and bridge sampling allow exploration across model spaces of different dimensionality, enabling fully Bayesian model selection and averaging.

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Markov Chain Monte Carlo for Bayesian Model Comparison
Rekodi ya mbinu ya kiajenda · bayesian / bayesian
  • Green, P. J. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82(4), 711–732. · DOI 10.1093/biomet/82.4.711
  • Meng, X.-L., & Wong, W. H. (1996). Simulating ratios of normalizing constants via a simple identity: A theoretical exploration. Statistica Sinica, 6(4), 831–860. · URL
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See alsoApproximate Bayesian Computationmachine-suggested · Relational suggestion, not evidence.Same method familyBayesian Model Averagingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketGibbs Samplingmachine-suggested · Relational suggestion, not evidence.Same method familyHamiltonian Monte Carlomachine-suggested · Relational suggestion, not evidence.Same method familyMCMCmachine-suggested · Relational suggestion, not evidence.

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