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| Байєсівська лінійна регресія× | Байєсівська регресія× | |
|---|---|---|
| Галузь | Баєсові методи | Баєсові методи |
| Родина | Bayesian methods | Bayesian methods |
| Рік появи≠ | 2013 (modern reference); foundations 18th–19th century | — |
| Автор методу≠ | Thomas Bayes / Pierre-Simon Laplace (foundations); modern workflow codified by Gelman et al. | — |
| Тип | Bayesian linear model | Bayesian linear model |
| Основоположне джерело | 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 | 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 |
| Інші назви | bayesian linear model, probabilistic linear regression, Bayesçi Doğrusal Regresyon | bayesian linear regression, probabilistic regression, bayesian regresyon |
| Пов'язані≠ | 4 | 2 |
| Підсумок≠ | Bayesian linear regression is a probabilistic extension of the ordinary linear model, introduced through Bayes' rule and formalised in its modern computational workflow by Gelman et al. (2013). Rather than returning a single point estimate for each coefficient, it combines a user-specified prior distribution with the likelihood of the observed data to produce a full posterior distribution over all parameters, from which credible intervals and posterior predictive distributions are derived. | Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off. |
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