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| Bayesiansk lineær regression× | Bayesiansk optimering× | |
|---|---|---|
| Fagområde≠ | Bayesiansk | Optimering |
| Familie≠ | Bayesian methods | Process / pipeline |
| Oprindelsesår≠ | 2013 (modern reference); foundations 18th–19th century | 1975 (foundational); 2012 (ML standard) |
| Ophavsperson≠ | Thomas Bayes / Pierre-Simon Laplace (foundations); modern workflow codified by Gelman et al. | Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012) |
| Type≠ | Bayesian linear model | Sequential model-based black-box optimization |
| Oprindelig kilde≠ | 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 | Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25. link ↗ |
| Aliasser≠ | bayesian linear model, probabilistic linear regression, Bayesçi Doğrusal Regresyon | Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO |
| Relaterede≠ | 4 | 2 |
| Resumé≠ | 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 Optimization is a sequential, model-based strategy for finding the optimum of expensive black-box functions with as few evaluations as possible. Rooted in the work of Mockus (1975) and brought to mainstream machine-learning practice by Snoek, Larochelle, and Adams (2012), it fits a probabilistic surrogate model — typically a Gaussian Process — to past observations and uses an acquisition function to decide where to probe next, balancing exploration of unknown regions with exploitation of promising ones. |
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