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Gibbs Sampling for Model Comparison

Gibbs sampling for model comparison is a Bayesian MCMC approach that simultaneously samples from the space of competing models and their parameters. By augmenting the Gibbs sampler with a discrete model-index variable, posterior model probabilities and Bayes factors are estimated from the resulting Markov chain without requiring separate runs per model.

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Gibbs Sampling for Bayesian Model Comparison

Taksonoomiline meetodikirje · bayesian / bayesian

Carlin, B. P. & Chib, S. (1995). Bayesian model choice via Markov chain Monte Carlo methods. Journal of the Royal Statistical Society, Series B, 57(3), 473-484. · DOI 10.1111/j.2517-6161.1995.tb02042.x
Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. · ISBN 978-1439840955

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Same method familyBayesian Model Averagingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketGibbs Samplingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMetropolis-Hastings for model comparisonmachine-suggested · Relational suggestion, not evidence.