手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| ロバストMCMC（Robust Markov Chain Monte Carlo）× | マルコフ連鎖モンテカルロ法 (MCMC)× | |
|---|---|---|
| 分野 | ベイズ | ベイズ |
| 系統 | Bayesian methods | Bayesian methods |
| 提唱年≠ | 2000s–2010s | — |
| 提唱者≠ | Roberts, Rosenthal and colleagues; extended by Atchade, Barp, Girolami and others | — |
| 種類≠ | Bayesian computational sampling | Posterior sampling algorithm |
| 原典≠ | Roberts, G. O. & Rosenthal, J. S. (2004). General state space Markov chains and MCMC algorithms. Probability Surveys, 1, 20–71. DOI ↗ | Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955 |
| 別名≠ | robust MCMC, outlier-robust MCMC, robust posterior sampling, misspecification-robust MCMC | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| 関連≠ | 5 | 3 |
| 概要≠ | Robust MCMC combines Markov chain Monte Carlo sampling with robustness techniques to produce reliable posterior inference when data contain outliers, when the assumed model is misspecified, or when the target distribution has heavy tails that cause standard samplers to mix poorly or yield distorted estimates. | Markov Chain Monte Carlo (MCMC) is a family of computational algorithms for sampling from complex probability distributions, most commonly the posterior distributions that arise in Bayesian inference. Rather than computing posteriors analytically — which is rarely possible for realistic models — MCMC constructs a Markov chain whose stationary distribution is the target posterior and draws dependent samples from it, enabling full probabilistic inference for virtually any model. |
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