Bayesian methods
Bayesian Model Averaging
Bayesian Model Averaging (BMA), formalised as a tutorial by Hoeting, Madigan, Raftery and Volinsky in 1999, addresses model uncertainty by averaging over all plausible model specifications rather than selecting a single best model. Each candidate model receives a posterior probability that reflects how well it fits the data given a prior, and predictions or coefficient estimates are formed as weighted averages across the entire model space. This approach reduces the bias and overconfidence that arise when a single selected model is treated as the true one.
Open in MethodMindSoonVideoSoon
Read the full method
Members only
Sign inSign in with a free account to read this section.
Sources
- Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382–401. DOI: 10.1214/ss/1009212519 ↗
- Zeugner, S. & Feldkircher, M. (2015). Bayesian Model Averaging Employing Fixed and Flexible Priors: The BMS Package for R. Journal of Statistical Software, 68(4), 1–37. DOI: 10.18637/jss.v068.i04 ↗
Related methods
Referenced by
Bayesian Model Averaging with Measurement ErrorBayesian model averaging with missing dataBayesian Stacking EnsembleDynamic Bayesian Model AveragingGibbs Sampling for Model ComparisonHierarchical Bayesian Model AveragingMCMCMCMC for Model ComparisonMetropolis-Hastings for model comparisonMultilevel Bayesian Model AveragingRobust Bayesian InferenceRobust Bayesian Model AveragingRobust Bayesian NetworkSpatial Bayesian Model AveragingTime series Bayesian model averaging