Comparar métodos

	Monte Carlo Sequencial com Dados Ausentes	Inferência Bayesiana com Dados Ausentes
Área	Bayesiano	Bayesiano
Família	Bayesian methods	Bayesian methods
Ano de origem	1993–2001	1976–1987
Autor original	Gordon, Salmond & Smith (particle filter, 1993); missing-data extensions formalised by Doucet et al. (2000s)	Rubin, D. B. (missing-data mechanisms); Tanner & Wong (data augmentation)
Tipo	Sequential Bayesian filtering / smoothing	Bayesian probabilistic model
Fonte seminal	Doucet, A., de Freitas, N., & Gordon, N. (Eds.) (2001). Sequential Monte Carlo Methods in Practice. Springer, New York. ISBN: 978-0387951461	Little, R. J. A. & Rubin, D. B. (2002). Statistical Analysis with Missing Data (2nd ed.). Wiley-Interscience. ISBN: 978-0471183860
Outros nomes	SMC with missing data, particle filter with missing observations, SMC missing observations, particle smoothing with incomplete data	Bayesian missing data analysis, Bayesian data augmentation, Bayesian imputation, missing data Bayesian model
Relacionados	6	6
Resumo	Sequential Monte Carlo (SMC) with missing data extends the standard particle filter to state-space models in which some observations are absent. When an observation is missing at a given time step the update step is simply skipped: particles are propagated forward through the transition model without reweighting, preserving exact Bayesian inference under any missing-data pattern as long as missingness is ignorable (missing at random or missing completely at random).	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.