เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| Metropolis-Hastings สำหรับการเปรียบเทียบโมเดล× | Sequential Monte Carlo× | |
|---|---|---|
| สาขาวิชา | เบย์ | เบย์ |
| ตระกูล | Bayesian methods | Bayesian methods |
| ปีกำเนิด≠ | 1970 (extended 1995) | 1993 (particle filter); 2006 (SMC samplers) |
| ผู้ริเริ่ม≠ | W. K. Hastings (1970); extended for model comparison by P. J. Green (1995) | Gordon, Salmond & Smith (particle filter); Del Moral, Doucet & Jasra (SMC samplers) |
| ประเภท≠ | MCMC-based model comparison | Sequential Bayesian computation |
| แหล่งต้นตำรับ≠ | Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1), 97-109. DOI ↗ | 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 ↗ |
| ชื่อเรียกอื่น | MH model comparison, Metropolis-Hastings Bayes factor estimation, reversible-jump Metropolis-Hastings, MH model selection | SMC, particle filter, sequential importance resampling, SMC sampler |
| ที่เกี่ยวข้อง≠ | 4 | 6 |
| สรุป≠ | Metropolis-Hastings for model comparison uses the Metropolis-Hastings MCMC algorithm to explore both parameter and model space simultaneously, producing posterior probabilities for competing models and enabling Bayes factor estimation without requiring closed-form marginal likelihoods. The canonical extension — reversible-jump MCMC by Green (1995) — handles models of different dimensionalities within a single sampler. | 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. |
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