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Что (кто) такое Bayes estimate - определение

BAYESIAN STATISTICAL INFERENCE METHOD IN WHICH THE PRIOR DISTRIBUTION IS ESTIMATED FROM THE DATA

Emprical Bayes; Empirical Bayes methods; Empirical Bayes estimator; Empirical Bayes estimators; Empirical Bayes estimate; Empirical Bayes estimates; Empirical Bayes; Empirical bayes method; Empirical Bayesian

Bayes estimator

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

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.

https://en.wikipedia.org/wiki/Bayes_estimator

Bayes' theorem

$Figure 1: Using a frequency box to show <math>P(\text{User}\mid \text{Positive}) </math> visually by comparison of shaded areas$
$Figure 4: Tree diagram illustrating the beetle example. ''R, C, P'' and <math> \overline{P} </math> are the events rare, common, pattern and no pattern. Percentages in parentheses are calculated. Three independent values are given, so it is possible to calculate the inverse tree.$

THEOREM DESCRIBING THE PROBABILITY OF AN EVENT BASED ON PRIOR KNOWLEDGE OF CONDITIONS THAT MIGHT BE RELATED TO THE EVENT

Bayes Theorem; Bayes' rule; Bayes' Theorem; Bayes' formula; Bayes rule; Bayes' Rule; Bayes theorum; Bayes Rule; Bayes formula; Baye's rule; Bayes's Theorem; Bayes's theorem; Baye's Theorem; Bayes's rule; Bayes Theorum; Bay's rule; Bayes' theorem of subjective probability; Bayes' Law; Bayes Law; Baye's Law; Bayes' law; Pretest odds; Bayes theorem; Bayes’ Law; Bayes’ theorem; Bayes’s theorem; Bayes decision theory; Bayes' decision theory; Baye's theorem; Bayesian theorem; Bayes's law; Bayes-Price theorem; Bayes–Price theorem; Bayesian formula

In probability theory and statistics, Bayes' theorem (alternatively Bayes' law or Bayes' rule), named after Thomas Bayes, describes the probability of an event, based on prior knowledge of conditions that might be related to the event. For example, if the risk of developing health problems is known to increase with age, Bayes' theorem allows the risk to an individual of a known age to be assessed more accurately (by conditioning it on their age) than simply assuming that the individual is typical of the population as a whole.

https://en.wikipedia.org/wiki/Bayes'_theorem

Bayes' theorem

$Figure 1: Using a frequency box to show <math>P(\text{User}\mid \text{Positive}) </math> visually by comparison of shaded areas$
$Figure 4: Tree diagram illustrating the beetle example. ''R, C, P'' and <math> \overline{P} </math> are the events rare, common, pattern and no pattern. Percentages in parentheses are calculated. Three independent values are given, so it is possible to calculate the inverse tree.$

THEOREM DESCRIBING THE PROBABILITY OF AN EVENT BASED ON PRIOR KNOWLEDGE OF CONDITIONS THAT MIGHT BE RELATED TO THE EVENT

Bayes Theorem; Bayes' rule; Bayes' Theorem; Bayes' formula; Bayes rule; Bayes' Rule; Bayes theorum; Bayes Rule; Bayes formula; Baye's rule; Bayes's Theorem; Bayes's theorem; Baye's Theorem; Bayes's rule; Bayes Theorum; Bay's rule; Bayes' theorem of subjective probability; Bayes' Law; Bayes Law; Baye's Law; Bayes' law; Pretest odds; Bayes theorem; Bayes’ Law; Bayes’ theorem; Bayes’s theorem; Bayes decision theory; Bayes' decision theory; Baye's theorem; Bayesian theorem; Bayes's law; Bayes-Price theorem; Bayes–Price theorem; Bayesian formula

¦ noun Statistics a theorem expressing the conditional probability of each of a set of possible causes for a given observed outcome in terms of the known probability of each cause and the conditional probability of the outcome of each cause.

Derivatives

Bayesian adjective

Origin

C19: named after the English mathematician Thomas Bayes.

Википедия

Empirical Bayes method

Empirical Bayes methods are procedures for statistical inference in which the prior probability distribution is estimated from the data. This approach stands in contrast to standard Bayesian methods, for which the prior distribution is fixed before any data are observed. Despite this difference in perspective, empirical Bayes may be viewed as an approximation to a fully Bayesian treatment of a hierarchical model wherein the parameters at the highest level of the hierarchy are set to their most likely values, instead of being integrated out. Empirical Bayes, also known as maximum marginal likelihood, represents a convenient approach for setting hyperparameters, but has been mostly supplanted by fully Bayesian hierarchical analyses since the 2000s with the increasing availability of well-performing computation techniques. It is still commonly used, however, for variational methods in Deep Learning, such as variational autoencoders, where latent variable spaces are high-dimensional.

https://en.wikipedia.org/wiki/Empirical Bayes method