विधियों की तुलना करें

	समय श्रृंखला सन्निकट बेयसियन संगणना	सीक्वेंशियल मोंटे कार्लो
क्षेत्र	बायेसियन	बायेसियन
परिवार	Bayesian methods	Bayesian methods
उद्भव वर्ष	2009	1993 (particle filter); 2006 (SMC samplers)
प्रवर्तक	Beaumont, Zhang & Balding (2002) for ABC; Toni et al. (2009) for dynamical/time-series extension	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
प्रकार	likelihood-free Bayesian inference	Sequential Bayesian computation
मौलिक स्रोत	Toni, T., Welch, D., Strelkowa, N., Ipsen, A. & Stumpf, M. P. H. (2009). Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems. Journal of the Royal Society Interface, 6(31), 187–202.	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	TS-ABC, time series ABC, likelihood-free inference for time series, ABC for dynamical systems	SMC, particle filter, sequential importance resampling, SMC sampler
संबंधित	6	6
सारांश	Time series ABC is a likelihood-free Bayesian inference method that estimates the posterior distribution of model parameters for dynamical or time-indexed systems by comparing summary statistics of simulated trajectories to those of the observed series, bypassing the need to evaluate an analytic likelihood. It is particularly valuable for complex mechanistic or stochastic models whose likelihoods are intractable.	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.