Krahasoni metodat

	Sekuencial Monte Carlo Dinamik	Monte Karlo Sekuencial
Fusha	Statistika bajesiane	Statistika bajesiane
Familja	Bayesian methods	Bayesian methods
Viti i origjinës	2006	1993 (particle filter); 2006 (SMC samplers)
Krijuesi	Del Moral, Doucet, Jasra	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Lloji	Sequential Monte Carlo sampler for dynamic settings	Sequential Bayesian computation
Burimi themelues	Del Moral, P., Doucet, A. & Jasra, A. (2006). Sequential Monte Carlo samplers. Journal of the Royal Statistical Society: Series B, 68(3), 411–436.	Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F - Radar and Signal Processing, 140(2), 107–113.
Emërtime të tjera	Dynamic SMC, SMC for dynamic models, sequential particle filter, dynamic particle sampler	SMC, particle filter, sequential importance resampling, SMC sampler
Të lidhura	6	6
Përmbledhja	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.	Sequential Monte Carlo (SMC) is a family of simulation-based algorithms that approximate evolving probability distributions by propagating and reweighting a cloud of weighted random draws called particles. It handles nonlinear, non-Gaussian models and streams of data naturally, making it the method of choice for real-time state estimation and posterior approximation over complex distributions.