Bayes risk - Definition. Was ist Bayes risk
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Was (wer) ist Bayes risk - 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

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.
Bayes' theorem         
  • Figure 1: Using a frequency box to show <math>P(\text{User}\mid \text{Positive}) </math> visually by comparison of shaded areas
  • function]] of ''x'' and ''y''.
  • Figure 2: A geometric visualisation of Bayes' theorem.
  • 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.
  • tree diagrams]].
  • Figure 6: A way to conceptualize event spaces generated by continuous random variables X and Y.
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.
Bayes' theorem         
  • Figure 1: Using a frequency box to show <math>P(\text{User}\mid \text{Positive}) </math> visually by comparison of shaded areas
  • function]] of ''x'' and ''y''.
  • Figure 2: A geometric visualisation of Bayes' theorem.
  • 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.
  • tree diagrams]].
  • Figure 6: A way to conceptualize event spaces generated by continuous random variables X and Y.
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.

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.