Bayesian model averaging
A tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors
Bibliographic Data
| ID | 23364352 |
|---|---|
| Authors | Jennifer A Hoeting (0000-0003-0414-0193), David Madigan (0000-0001-9754-1011), Adrian E Raftery (0000-0002-6589-301X), Chris Volinsky, Chris T Volinsky |
| Year | 1999 |
| Volume | 14 |
| Issue | 4 |
| Publication date | 1999-11-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Statistical Science (JOURNAL) |
| Journal identifiers | ISSN: 0883-4237 • E-ISSN: 2168-8745 |
| Publisher | Institute of Mathematical Statistics (PUBLISHER • US) |
| DOI | 10.1214/ss/1009212519 |
| OpenAlex | W1603903339 |
| Language | EN |
| Citations received | 160 |
| References cited | 95 |
Standard statistical practice ignores model uncertainty. Data analysts typically select a model from some class of models and then proceed as if the selected model had generated the data. This approach ignores the uncertainty in model selection, leading to over-confident inferences and decisions that are more risky than one thinks they are. Bayesian model averaging (BMA)provides a coherent mechanism for accounting for this model uncertainty. Several methods for implementing BMA have recently emerged. We discuss these methods and present a number of examples.In these examples, BMA provides improved out-of-sample predictive performance. We also provide a catalogue of currently available BMA software.
Bayesian inference · Bayesian probability · Class (philosophy) · Econometrics · George (robot) · Machine learning · Model selection · Sample (material) · Selection (genetic algorithm) · Advanced Statistical Methods and Models · Artificial Intelligence · Computer Science · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference
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| Unique citing works | 160 |
|---|---|
| Citations per year | 5,93 |
| Citation span | 1999 - 2026 (28) |
| Citation velocity | current |
| Highly cited | Yes |
| Citation types | Neutral: 98 |