Bayesian methodsBayesian / computational
Bayesian Inference with Missing Data
Bayesian inference with missing data treats unobserved values as unknown parameters and integrates them out of the posterior distribution. Rather than deleting or ad hoc imputing incomplete records, the method jointly models observed and missing data under an explicit missing-data mechanism, producing fully calibrated posterior uncertainty that honestly reflects what the data cannot tell us.
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Sources
- Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley-Interscience. ISBN: 978-0471183860
- 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
Related methods
Referenced by
Approximate Bayesian Computation with Missing DataBayesian Hierarchical Model with Missing DataBayesian model averaging with missing dataBootstrap Simulation with Missing DataGibbs Sampling with Missing DataHamiltonian Monte Carlo with Missing DataKalman Filter with Missing DataMCMC with missing dataMetropolis-Hastings with Missing DataMonte Carlo Simulation with Missing DataParticle Filter with Missing DataSequential Monte Carlo with Missing DataVariational Inference with Missing Data