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What (who) is Bayesian estimate - definition
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
Bayesian inference
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
Bayesian inference is a 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 inference is an important technique in statistics, and especially in mathematical statistics.
Recursive Bayesian estimation
Bayesian filter; Bayesian filtering; Sequential bayesian filtering; Bayes filter; Belief filter
In probability theory, statistics, and machine learning, recursive Bayesian estimation, also known as a Bayes filter, is a general probabilistic approach for estimating an unknown probability density function (PDF) recursively over time using incoming measurements and a mathematical process model. The process relies heavily upon mathematical concepts and models that are theorized within a study of prior and posterior probabilities known as Bayesian statistics.
Bayesian optimization
OPTIMIZATION TECHNIQUE FOR UNDIFFERENTIABLE, BLACK-BOX FUNCTIONS
Bayesian Optimization; Applications of Bayesian optimization; Bayesian optimisation
Bayesian optimization is a sequential design strategy for global optimization of black-box functions that does not assume any functional forms. It is usually employed to optimize expensive-to-evaluate functions.
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.
