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Variational Inference with Measurement Error

Variational inference with measurement error is a scalable Bayesian approach that simultaneously estimates model parameters and latent true covariates when observed variables are contaminated by noise. Rather than sampling the posterior via MCMC, it finds the closest tractable distribution to the true posterior by maximising the evidence lower bound (ELBO), making it applicable to large datasets where full MCMC is too costly.

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Variational Bayesian Inference for Models with Measurement Error

Taxonomisk metodpost · bayesian / bayesian

Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017). Variational inference: A review for statisticians. Journal of the American Statistical Association, 112(518), 859–877. · DOI 10.1080/01621459.2017.1285773
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

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Taxonomic bucketApproximate Bayesian Computation with Measurement Errormachine-suggested · Relational suggestion, not evidence.Taxonomic bucketBayesian Inference with Measurement Errormachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMCMC with Measurement Errormachine-suggested · Relational suggestion, not evidence.Same method familyVariational Inferencemachine-suggested · Relational suggestion, not evidence.