Сравнение на методи

	Динамично последователно Монте Карло	Динамично Байесово извеждане
Област	Бейсови методи	Бейсови методи
Семейство	Bayesian methods	Bayesian methods
Година на възникване	2006	1989–1997
Създател	Del Moral, Doucet, Jasra	West & Harrison (dynamic linear models); Dean & Kanazawa (dynamic Bayesian networks)
Тип	Sequential Monte Carlo sampler for dynamic settings	Bayesian sequential / online inference framework
Основополагащ източник	Del Moral, P., Doucet, A. & Jasra, A. (2006). Sequential Monte Carlo samplers. Journal of the Royal Statistical Society: Series B, 68(3), 411–436.	West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259
Други названия	Dynamic SMC, SMC for dynamic models, sequential particle filter, dynamic particle sampler	online Bayesian inference, sequential Bayesian updating, recursive Bayesian estimation, dynamic Bayesian updating
Свързани	6	6
Резюме	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.	Dynamic Bayesian inference is a framework for performing Bayesian updating sequentially as new observations arrive over time. Rather than fitting a static model to a fixed dataset, it tracks how a posterior distribution over latent states or parameters evolves step by step, combining a prior with each new likelihood to produce an updated posterior that propagates forward through time.