방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 순차 몬테카를로 (Sequential Monte Carlo, SMC)× | 근사 베이즈 계산× | |
|---|---|---|
| 분야≠ | 베이지안 | 시뮬레이션 |
| 계열≠ | Bayesian methods | Process / pipeline |
| 기원 연도≠ | 1993 (particle filter); 2006 (SMC samplers) | 2002 |
| 창시자≠ | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) | — |
| 유형≠ | Sequential Bayesian computation | Simulation-based Bayesian inference |
| 원전≠ | 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 ↗ |
| 별칭 | SMC, particle filter, sequential importance resampling, SMC sampler | ABC, likelihood-free inference, simulation-based inference, Yaklaşık Bayesçi Hesaplama (ABC) |
| 관련≠ | 6 | 5 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
|
|