Methoden vergleichen

	Hierarchische Approximate Bayesian Computation	Sequentielle Monte-Carlo-Methoden
Fachgebiet	Bayes-Statistik	Bayes-Statistik
Familie	Bayesian methods	Bayesian methods
Entstehungsjahr	2009–2010	1993 (particle filter); 2006 (SMC samplers)
Urheber	Toni, Welch, Strelkowa, Ipsen & Stumpf (building on Pritchard et al. 1999 and Beaumont et al. 2002)	Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers)
Typ	simulation-based Bayesian inference	Sequential Bayesian computation
Wegweisende Quelle	Toni, T. & Stumpf, M. P. H. (2010). Simulation-based model selection for dynamical systems in systems and population biology. Bioinformatics, 26(1), 104–110.	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.
Aliasnamen	hierarchical ABC, ABC for hierarchical models, multilevel ABC, population ABC	SMC, particle filter, sequential importance resampling, SMC sampler
Verwandt	4	6
Zusammenfassung	Hierarchical ABC is a likelihood-free Bayesian inference method designed for multilevel data structures in which individual-level parameters are themselves drawn from a population-level distribution. By combining simulation-based rejection sampling with hierarchical pooling, it recovers both within-group and between-group posterior distributions without requiring a tractable likelihood function.	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.