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Multilevel Gibbs Sampling

Multilevel Gibbs sampling applies the Gibbs MCMC algorithm to hierarchical (multilevel) Bayesian models, cycling through the conditional distributions of group-level parameters and population-level hyperparameters in turn. This exploits the conditional independence structure of the hierarchy to draw exact or near-exact samples from a posterior that would otherwise be analytically intractable.

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Multilevel Gibbs Sampling for Hierarchical Bayesian Models

Rekodi ya mbinu ya kiajenda · bayesian / bayesian

Gelman, A. & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. · ISBN 978-0521686891
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 Hierarchical Modelmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketGibbs Samplingmachine-suggested · Relational suggestion, not evidence.Same method familyHamiltonian Monte Carlomachine-suggested · Relational suggestion, not evidence.Taxonomic bucketHierarchical Bayesian Inferencemachine-suggested · Relational suggestion, not evidence.Same method familyMetropolis-Hastings Algorithmmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMultilevel MCMCmachine-suggested · Relational suggestion, not evidence.