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Sequential Monte Carlo with Missing Data

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).

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Taksonoomiline meetodikirje · bayesian / bayesian

Doucet, A., de Freitas, N., & Gordon, N. (Eds.) (2001). Sequential Monte Carlo Methods in Practice. Springer, New York. · ISBN 978-0387951461
Chopin, N., & Papaspiliopoulos, O. (2020). An Introduction to Sequential Monte Carlo. Springer, Cham. · DOI 10.1007/978-3-030-47845-2

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Taxonomic bucketBayesian Inference with Missing Datamachine-suggested · Relational suggestion, not evidence.Taxonomic bucketDynamic Sequential Monte Carlomachine-suggested · Relational suggestion, not evidence.Taxonomic bucketGibbs Sampling with Missing Datamachine-suggested · Relational suggestion, not evidence.Taxonomic bucketKalman Filter with Missing Datamachine-suggested · Relational suggestion, not evidence.Same method familyParticle Filtermachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSequential Monte Carlomachine-suggested · Relational suggestion, not evidence.