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Hamiltonian Monte Carlo with Measurement Error

Hamiltonian Monte Carlo (HMC) with measurement error is a Bayesian computational strategy for fitting models where one or more covariates are observed with noise. HMC samples jointly from the posterior over model parameters and the unobserved true covariate values, using gradient-based proposals that explore the high-dimensional posterior efficiently and avoid the slow random-walk behaviour of standard Metropolis sampling.

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Hamiltonian Monte Carlo for Bayesian Measurement Error Models

Dossier de méthode taxonomique · 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 and Hall/CRC. · ISBN 978-1584886334
Neal, R. M. (2011). MCMC using Hamiltonian dynamics. In S. Brooks, A. Gelman, G. Jones, & X.-L. Meng (Eds.), Handbook of Markov Chain Monte Carlo (pp. 113-162). CRC Press. · URL

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Taxonomic bucketBayesian Inference with Measurement Errormachine-suggested · Relational suggestion, not evidence.Taxonomic bucketGibbs Sampling with Measurement Errormachine-suggested · Relational suggestion, not evidence.Same method familyHamiltonian Monte Carlomachine-suggested · Relational suggestion, not evidence.Taxonomic bucketKalman Filter with Measurement Errormachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMCMC with Measurement Errormachine-suggested · Relational suggestion, not evidence.Taxonomic bucketVariational Inference with Measurement Errormachine-suggested · Relational suggestion, not evidence.