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| Robust Bayesian Model Averaging× | Markov Chain Monte Carlo (MCMC)× | |
|---|---|---|
| Fagområde | Bayesiansk | Bayesiansk |
| Familie | Bayesian methods | Bayesian methods |
| Oprindelsesår≠ | 1999–2012 | — |
| Ophavsperson≠ | Hoeting, Madigan, Raftery, Volinsky (BMA); robustness extensions by Ley & Steel and others | — |
| Type≠ | Bayesian model selection and averaging | Posterior sampling algorithm |
| Oprindelig kilde≠ | Hoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999). Bayesian model averaging: A tutorial. Statistical Science, 14(4), 382–401. link ↗ | 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 |
| Aliasser≠ | robust BMA, outlier-robust BMA, robust model averaging, heavy-tailed BMA | markov chain monte carlo, MCMC sampling, MCMC (Markov Zinciri Monte Carlo) |
| Relaterede≠ | 6 | 3 |
| Resumé≠ | Robust Bayesian model averaging extends standard BMA by replacing sensitive conjugate priors with heavy-tailed or mixture priors (e.g., mixtures of g-priors), and optionally robust likelihoods, so that posterior model probabilities and averaged estimates remain stable when data contain outliers, influential observations, or when the prior on model parameters would otherwise dominate the results. | 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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