Vertaile menetelmiä

	Differentiaalikehitys – globaali stokastinen optimoija	Bayesilainen regressio
Tieteenala	Optimointi	Bayesilainen tilastotiede
Menetelmäperhe	Process / pipeline	Bayesian methods
Syntyvuosi	1997	—
Kehittäjä	Rainer Storn & Kenneth Price	—
Tyyppi	Population-based stochastic metaheuristic	Bayesian linear model
Alkuperäislähde	Storn, R. & Price, K. (1997). Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, 11(4), 341–359.	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
Rinnakkaisnimet	DE algorithm, Diferansiyel Evrim (DE), DE optimization	bayesian linear regression, probabilistic regression, bayesian regresyon
Liittyvät	5	2
Tiivistelmä	Differential Evolution (DE), introduced by Rainer Storn and Kenneth Price in 1997, is a population-based stochastic optimisation algorithm designed for continuous parameter spaces. It generates candidate solutions by combining vector differences between existing population members, making it a powerful and parameter-lean alternative to Genetic Algorithms and Particle Swarm Optimisation when the search landscape is non-convex, multimodal, or poorly suited to gradient-based methods.	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.