Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Monte Carlo Secvențial× | Calculul bayesian aproximativ× | |
|---|---|---|
| Domeniu≠ | Bayesian | Simulare |
| Familie≠ | Bayesian methods | Process / pipeline |
| Anul apariției≠ | 1993 (particle filter); 2006 (SMC samplers) | 2002 |
| Autorul original≠ | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) | — |
| Tip≠ | Sequential Bayesian computation | Simulation-based Bayesian inference |
| Sursa seminală≠ | 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. DOI ↗ | Beaumont, M.A., Zhang, W. & Balding, D.J. (2002). Approximate Bayesian Computation in Population Genetics. Genetics, 162(4), 2025-2035. DOI ↗ |
| Denumiri alternative | SMC, particle filter, sequential importance resampling, SMC sampler | ABC, likelihood-free inference, simulation-based inference, Yaklaşık Bayesçi Hesaplama (ABC) |
| Înrudite≠ | 6 | 5 |
| Rezumat≠ | 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. | Approximate Bayesian Computation (ABC) is a family of simulation-based inference methods that estimate posterior distributions without requiring an analytically tractable likelihood function. Introduced by Beaumont, Zhang and Balding (2002) in the context of population genetics, ABC replaced the intractable likelihood with repeated model simulation and a comparison of summary statistics between simulated and observed data. |
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