Salīdzināt metodes

	Sekvenciālā Monte Karlo metode ar trūkstošiem datiem	Dinamiskā secīgā Montekarlo metode
Nozare	Bajesa metodes	Bajesa metodes
Saime	Bayesian methods	Bayesian methods
Izcelsmes gads	1993–2001	2006
Autors	Gordon, Salmond & Smith (particle filter, 1993); missing-data extensions formalised by Doucet et al. (2000s)	Del Moral, Doucet, Jasra
Tips	Sequential Bayesian filtering / smoothing	Sequential Monte Carlo sampler for dynamic settings
Pirmavots	Doucet, A., de Freitas, N., & Gordon, N. (Eds.) (2001). Sequential Monte Carlo Methods in Practice. Springer, New York. ISBN: 978-0387951461	Del Moral, P., Doucet, A. & Jasra, A. (2006). Sequential Monte Carlo samplers. Journal of the Royal Statistical Society: Series B, 68(3), 411–436.
Citi nosaukumi	SMC with missing data, particle filter with missing observations, SMC missing observations, particle smoothing with incomplete data	Dynamic SMC, SMC for dynamic models, sequential particle filter, dynamic particle sampler
Saistītās	6	6
Kopsavilkums	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).	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.