Порівняння методів

	TrueSkill: Байєсівський рейтинг навичок для конкурентних рейтингів	Байєсівський висновок
Галузь	Прийняття рішень	Статистика
Родина	Regression model	Bayesian methods
Рік появи	2007	1763
Автор методу	Ralf Herbrich, Tom Minka & Thore Graepel	Thomas Bayes; Pierre-Simon Laplace
Тип	Probabilistic ranking model	Probabilistic inference paradigm
Основоположне джерело	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.
Інші назви	Bayesian Skill Rating, TrueSkill Ranking System, Gaussian Skill Model, Beceri Derecelendirme Modeli	Bayes inference, Bayesian statistics, Bayesian updating, posterior inference
Пов'язані	3	3
Підсумок	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.