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	Sekwencyjne metody Monte Carlo z błędem pomiaru	Dynamic Bayesian Inference
Dziedzina	Statystyka bayesowska	Statystyka bayesowska
Rodzina	Bayesian methods	Bayesian methods
Rok powstania	1993–2001	1989–1997
Twórca	Gordon, Salmond & Smith (1993); extended by Doucet, de Freitas & Gordon (2001)	West & Harrison (dynamic linear models); Dean & Kanazawa (dynamic Bayesian networks)
Typ	Sequential Bayesian filtering	Bayesian sequential / online inference framework
Źródło pierwotne	Doucet, A., de Freitas, N., & Gordon, N. (Eds.). (2001). Sequential Monte Carlo Methods in Practice. Springer New York. ISBN: 978-0-387-95146-1	West, M. & Harrison, J. (1997). Bayesian Forecasting and Dynamic Models (2nd ed.). Springer. ISBN: 978-0387947259
Inne nazwy	SMC with measurement error, particle filter with noisy observations, SMC state-space measurement error, sequential particle filtering with observation noise	online Bayesian inference, sequential Bayesian updating, recursive Bayesian estimation, dynamic Bayesian updating
Pokrewne	6	6
Podsumowanie	Sequential Monte Carlo (SMC) with measurement error is a particle-based Bayesian filtering method for tracking hidden states in dynamical systems when observations are corrupted by noise. It propagates a weighted cloud of particles through time, updating weights at each step to reflect how well each particle explains the noisy measurement, and produces a full posterior distribution over the latent state at every time point.	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.