Comparar métodos

	Monte Carlo secuencial para series temporales	Monte Carlo Secuencial
Campo	Bayesiano	Bayesiano
Familia	Bayesian methods	Bayesian methods
Año de origen	1993	1993 (particle filter); 2006 (SMC samplers)
Autor original	Gordon, Salmond & Smith	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Tipo	Sequential Bayesian filtering algorithm	Sequential Bayesian computation
Fuente seminal	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.	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.
Alias	particle filter, time series SMC, sequential particle filtering, bootstrap particle filter	SMC, particle filter, sequential importance resampling, SMC sampler
Relacionados	5	6
Resumen	Time series sequential Monte Carlo (SMC), commonly called the particle filter, is a Bayesian simulation method that tracks the hidden state of a dynamical system as observations arrive one at a time. A cloud of weighted random samples — particles — is propagated forward through the system dynamics, reweighted by how well each particle explains the new observation, and periodically resampled to keep the representation concentrated on plausible states.	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.