Сравнение на методи

	TrueSkill: Байесов рейтинг на уменията за състезателни класирания	Байесовско заключение
Област	Вземане на решения	Статистика
Семейство	Regression model	Bayesian methods
Година на възникване	2007	1763
Създател	Ralf Herbrich, Tom Minka & Thore Graepel	Thomas Bayes; Pierre-Simon Laplace
Тип	Probabilistic ranking model	Probabilistic inference paradigm
Основополагащ източник	Herbrich, R., Minka, T., & Graepel, T. (2007). TrueSkill: A Bayesian skill rating system. Advances in Neural Information Processing Systems, 19, 569–576.	Bayes, T. (1763). An essay towards solving a problem in the doctrine of chances. Philosophical Transactions of the Royal Society of London, 53, 370–418.
Други названия	Bayesian Skill Rating, TrueSkill Ranking System, Gaussian Skill Model, Beceri Derecelendirme Modeli	Bayes inference, Bayesian statistics, Bayesian updating, posterior inference
Свързани	3	3
Резюме	TrueSkill is a Bayesian skill rating system developed by Herbrich, Minka, and Graepel at Microsoft Research and introduced at NeurIPS 2006. It represents each player's skill as a Gaussian distribution parameterized by a mean (estimated skill) and a variance (uncertainty). After each match outcome, the system updates these distributions via approximate message passing, yielding a principled ranking that handles team games, draws, and partial observations in online settings.	Bayesian inference is a statistical paradigm in which probability represents degrees of belief rather than long-run frequencies. It encodes prior knowledge about parameters in a prior distribution, combines that prior with the likelihood of observed data via Bayes' theorem, and produces a posterior distribution that quantifies updated uncertainty. The foundational theorem was published posthumously by Thomas Bayes in 1763 and subsequently systematized by Pierre-Simon Laplace in his 1812 Théorie analytique des probabilités.