Sammenlign metoder

	Bayesiansk modellgjennomsnitt	Bayesiansk regresjon	Variasjonsinferens
Fagfelt	Bayesiansk	Bayesiansk	Bayesiansk
Familie	Bayesian methods	Bayesian methods	Bayesian methods
Opprinnelsesår	1999	—	1999
Opphavsperson	Hoeting, Madigan, Raftery & Volinsky	—	Jordan, Ghahramani, Jaakkola & Saul
Type	Bayesian model averaging	Bayesian linear model	Approximate Bayesian inference
Opprinnelig kilde	Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382–401.	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	Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999). An introduction to variational methods for graphical models. Machine Learning, 37(2), 183–233.
Alias	BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA)	bayesian linear regression, probabilistic regression, bayesian regresyon	VI, variational Bayes, VB, mean-field variational inference
Relaterte	5	2	4
Sammendrag	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.	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.	Variational inference (VI) is a family of techniques that turn Bayesian posterior computation into an optimisation problem. Instead of drawing samples from the exact posterior — as Markov chain Monte Carlo does — VI posits a simpler, tractable family of distributions and finds the member of that family closest to the true posterior by maximising the evidence lower bound (ELBO). Introduced in its modern graphical-model form by Jordan, Ghahramani, Jaakkola and Saul (1999) and given a comprehensive statistical treatment by Blei, Kucukelbir and McAuliffe (2017), VI is now the standard scalable inference engine in probabilistic machine learning.