เปรียบเทียบวิธี
ดูวิธีที่เลือกเทียบกันแบบเคียงข้าง แถวที่ต่างกันจะถูกเน้นไว้
| การอนุมานแบบแปรผันที่ทนทาน (Robust Variational Inference)× | Markov Chain Monte Carlo (MCMC)× | |
|---|---|---|
| สาขาวิชา≠ | เบย์ | การจำลอง |
| ตระกูล≠ | Bayesian methods | Process / pipeline |
| ปีกำเนิด≠ | 2008-2018 | 1953 (Metropolis-Hastings); 1984 (Gibbs) |
| ผู้ริเริ่ม≠ | Fujisawa & Eguchi (2008); Futami, Sato & Sugiyama (2018) | Metropolis et al. (1953); Gibbs sampler formalised by Geman & Geman (1984) |
| ประเภท≠ | Robust approximate Bayesian inference | Simulation-based Bayesian inference / numerical integration |
| แหล่งต้นตำรับ≠ | Futami, F., Sato, I. & Sugiyama, M. (2018). Variational inference based on robust divergences. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 84:813-822. link ↗ | Gelman, A., Carlin, J.B., Stern, H.S., Dunson, D.B., Vehtari, A. & Rubin, D.B. (2013). Bayesian Data Analysis (3rd ed.). Chapman & Hall/CRC. DOI ↗ |
| ชื่อเรียกอื่น | RVI, robust VI, outlier-robust variational Bayes, power-divergence variational inference | MCMC, Metropolis-Hastings, Gibbs sampling, Markov Zinciri Monte Carlo (MCMC — Metropolis-Hastings, Gibbs) |
| ที่เกี่ยวข้อง≠ | 6 | 5 |
| สรุป≠ | Robust variational inference (RVI) extends standard variational inference by replacing the Kullback-Leibler divergence with a divergence measure that is less sensitive to outliers and model misspecification — such as the beta-divergence or a Renyi-type divergence. This yields posterior approximations that remain well-behaved even when a fraction of the data departs from the assumed model. | Markov Chain Monte Carlo (MCMC) is a family of simulation algorithms that constructs a Markov chain whose stationary distribution is the target posterior, enabling Bayesian inference and high-dimensional integral computation that would otherwise be analytically intractable. Pioneered by Metropolis and colleagues in 1953 and extended by Hastings in 1970, MCMC underpins modern Bayesian statistics. The two most widely used variants are Metropolis-Hastings, which proposes moves from a general proposal distribution, and Gibbs sampling, which draws each parameter in turn from its full conditional distribution. |
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