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MCMC with Measurement Error

MCMC with measurement error applies Markov chain Monte Carlo sampling to Bayesian models that explicitly account for the fact that covariates or outcomes are observed with error. By treating the true, unobserved values as latent variables and sampling their joint posterior alongside all other parameters, the method corrects for attenuation bias and produces valid inference even when some variables cannot be measured exactly.

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Markov Chain Monte Carlo with Measurement Error Models

Rekodi ya mbinu ya kiajenda · bayesian / bayesian

Carroll, R. J., Ruppert, D., Stefanski, L. A. & Crainiceanu, C. M. (2006). Measurement Error in Nonlinear Models: A Modern Perspective (2nd ed.). Chapman & Hall/CRC. · ISBN 978-1584886334
Richardson, S. & Gilks, W. R. (1993). A Bayesian approach to measurement error problems in epidemiology using conditional independence models. American Journal of Epidemiology, 138(6), 430-442. · URL

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Taxonomic bucketBayesian Inference with Measurement Errormachine-suggested · Relational suggestion, not evidence.Same method familyBayesian Regressionmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketGibbs Samplingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketHierarchical Bayesian Inferencemachine-suggested · Relational suggestion, not evidence.Same method familyMCMCmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMetropolis-Hastings with measurement errormachine-suggested · Relational suggestion, not evidence.