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non-Bayesian risk - translation to russian
ESTIMATOR OR DECISION RULE THAT MINIMIZES THE POSTERIOR EXPECTED VALUE OF A LOSS FUNCTION
Bayesian estimation; Bayesian estimate; Bayesian estimator; Bayes risk; Bayes estimation; Bayesian decision theory; Generalized Bayes estimator; Asymptotic efficiency (Bayes); Bayes action; Posterior mean; Bayesian risk
non-Bayesian risk
небайесовский риск
bayesian analysis
METHOD OF STATISTICAL INFERENCE IN WHICH BAYES' THEOREM IS USED TO UPDATE THE PROBABILITY FOR A HYPOTHESIS AS MORE EVIDENCE OR INFORMATION BECOMES AVAILABLE
Bayesian analysis; Screening test fallacy; Bayesian Inference; Bayesian method; Bayesian methods; Bayesian modeling; Bayesian updating; Bayesian learning; Bayesian statistical analysis; Bayesian investor; Baysein statistics; Bayesian update; Bayesian analyses; Bayesian confirmation theory; Bayesian Algorithms; Applications of Bayesian inference; Confirmation theory
общая лексика
байсов(ский) анализ
Bayesian analysis
METHOD OF STATISTICAL INFERENCE IN WHICH BAYES' THEOREM IS USED TO UPDATE THE PROBABILITY FOR A HYPOTHESIS AS MORE EVIDENCE OR INFORMATION BECOMES AVAILABLE
Bayesian analysis; Screening test fallacy; Bayesian Inference; Bayesian method; Bayesian methods; Bayesian modeling; Bayesian updating; Bayesian learning; Bayesian statistical analysis; Bayesian investor; Baysein statistics; Bayesian update; Bayesian analyses; Bayesian confirmation theory; Bayesian Algorithms; Applications of Bayesian inference; Confirmation theory
лют, байесовский анализ
Definition
Деньги не пахнут
(неодобр.) от неразборчивом отношении к тому, каким путем получены деньги. Выражение - калька с лат. pecunia non olet, которое является высказыванием императора Веспасиана. Когда сын Веспасиана упрекнул его в том, что он ввел налог на общественные уборные, император поднес к его носу первые деньги, поступившие от этого налога, и спросил, пахнут ли они. Тит дал отрицательный ответ.
Wikipedia
Bayes estimator
In estimation theory and decision theory, a Bayes estimator or a Bayes action is an estimator or decision rule that minimizes the posterior expected value of a loss function (i.e., the posterior expected loss). Equivalently, it maximizes the posterior expectation of a utility function. An alternative way of formulating an estimator within Bayesian statistics is maximum a posteriori estimation.
