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	टाइम सीरीज़ MCMC	सीक्वेंशियल मोंटे कार्लो
क्षेत्र	बायेसियन	बायेसियन
परिवार	Bayesian methods	Bayesian methods
उद्भव वर्ष	1994–1997	1993 (particle filter); 2006 (SMC samplers)
प्रवर्तक	Carter & Kohn; West & Harrison	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
प्रकार	Bayesian posterior sampling for time-ordered data	Sequential Bayesian computation
मौलिक स्रोत	Carter, C. K. & Kohn, R. (1994). On Gibbs sampling for state space models. Biometrika, 81(3), 541–553.	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.
उपनाम	MCMC time series, Bayesian time series MCMC, time series posterior sampling, sequential Bayesian MCMC	SMC, particle filter, sequential importance resampling, SMC sampler
संबंधित	6	6
सारांश	Time series MCMC applies Markov chain Monte Carlo methods to Bayesian inference over time-ordered data. Rather than optimising a single parameter estimate, it draws samples from the full joint posterior of parameters and latent states, yielding probability distributions that honestly reflect uncertainty about dynamics, trends, and seasonal patterns across every time point.	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.