Method evidence record

Dynamic Sequential Monte Carlo

Dynamic Sequential Monte Carlo (Dynamic SMC) is a Bayesian computational method that maintains and updates a population of weighted samples — particles — as new observations arrive over time. It propagates particles through a dynamic system model, reweights them by how well they match the observed data, and periodically resamples to concentrate effort on high-probability regions, yielding online posterior inference for state-space and time-evolving models.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Dynamic Sequential Monte Carlo Sampler

Taxonomic method record · bayesian / bayesian

Del Moral, P., Doucet, A. & Jasra, A. (2006). Sequential Monte Carlo samplers. Journal of the Royal Statistical Society: Series B, 68(3), 411–436. · DOI 10.1111/j.1467-9868.2006.00553.x
Doucet, A., de Freitas, N. & Gordon, N. (Eds.) (2001). Sequential Monte Carlo Methods in Practice. Springer. · ISBN 978-0387951461

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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Taxonomic bucketDynamic Bayesian Inferencemachine-suggested · Relational suggestion, not evidence.Taxonomic bucketGibbs Samplingmachine-suggested · Relational suggestion, not evidence.Same method familyHamiltonian Monte Carlomachine-suggested · Relational suggestion, not evidence.Taxonomic bucketKalman Filtermachine-suggested · Relational suggestion, not evidence.Same method familyParticle Filtermachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSequential Monte Carlomachine-suggested · Relational suggestion, not evidence.