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Bayesian sampling plan - перевод на русский

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

$Diagram illustrating event space <math>\Omega</math> in general formulation of Bayesian inference. Although this diagram shows discrete models and events, the continuous case may be visualized similarly using probability densities.$

Bayesian sampling plan

байесовский план выборочного контроля

bayesian analysis

общая лексика

байсов(ский) анализ

Bayesian analysis

лют, байесовский анализ

Определение

Nyquist Theorem

<communications> A theorem stating that when an analogue waveform is digitised, only the frequencies in the waveform below half the sampling frequency will be recorded. In order to reconstruct (interpolate) a signal from a sequence of samples, sufficient samples must be recorded to capture the peaks and troughs of the original waveform. If a waveform is sampled at less than twice its frequency the reconstructed waveform will effectively contribute only noise. This phenomenon is called "aliasing" (the high frequencies are "under an alias"). This is why the best digital audio is sampled at 44,000 Hz - twice the average upper limit of human hearing. The Nyquist Theorem is not specific to digitised signals (represented by discrete amplitude levels) but applies to any sampled signal (represented by discrete time values), not just sound. {Nyquist (http://geocities.com/bioelectrochemistry/nyquist.htm)} (the man, somewhat inaccurate). (2003-10-21)

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Википедия

Bayesian inference

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. Bayesian updating is particularly important in the dynamic analysis of a sequence of data. Bayesian inference has found application in a wide range of activities, including science, engineering, philosophy, medicine, sport, and law. In the philosophy of decision theory, Bayesian inference is closely related to subjective probability, often called "Bayesian probability".

https://en.wikipedia.org/wiki/Bayesian inference

Как переводится Bayesian sampling plan на Русский язык