Entrez un mot ou une phrase dans n'importe quelle langue 👆
Langue:
Traduction et analyse de mots par intelligence artificielle ChatGPT
Sur cette page, vous pouvez obtenir une analyse détaillée d'un mot ou d'une phrase, réalisée à l'aide de la meilleure technologie d'intelligence artificielle à ce jour:
- comment le mot est utilisé
- fréquence d'utilisation
- il est utilisé plus souvent dans le discours oral ou écrit
- options de traduction de mots
- exemples d'utilisation (plusieurs phrases avec traduction)
- étymologie
double-sampling estimate - traduction vers russe
Raygor Estimate Graph; Raygor Readability Estimate
double-sampling estimate
статистика
оценка
полученная по двум выборкам
snowball sampling
NONPROBABILITY SAMPLING TECHNIQUE
Snowball sample; Respondent-driven sampling; Snowball method; Snowballed sample
выборка типа "снежный ком"; эмпирическая выборка, не имеющая вероятностного обоснования, формируется, когда трудно очертить границы генеральной совокупности путем целенаправленного отбора экспертов и так называемых "редких элементов", которые после интервью могут указать следующий элемент и так далее (используется при изучении закрытых социальных групп - религиозных сект, банд и т.п.).
sampling theorem
![Subsampled image showing a [[Moiré pattern]]](https://commons.wikimedia.org/wiki/Special:FilePath/Moire pattern of bricks small.jpg?width=200 "Subsampled image showing a [[Moiré pattern]]")
.svg?width=200 "x}}.")
THEOREM
Nyquist theorem; Shannon sampling theorem; Nyquist sampling theorem; Nyquist's theorem; Shannon-Nyquist sampling theorem; Nyquist-Shannon Sampling Theorem; Nyqvist-Shannon sampling theorem; Sampling theorem; Nyquist Sampling Theorem; Nyquist-Shannon sampling theorem; Nyquist–Shannon theorem; Nyquist–Shannon Theorem; Nyquist Theorem; Shannon-Nyquist theorem; Nyquist sampling; Nyquist's law; Nyquist law; Coherent sampling; Nyqvist limit; Raabe condition; Nyquist-Shannon Theorem; Nyquist-Shannon theorem; Nyquist noise theorem; Shannon–Nyquist theorem; Kotelnikov-Shannon theorem; Kotelnikov–Shannon theorem; Nyquist-Shannon; Kotelnikov theorem; Nyquist's sampling theorem; Sampling Theorem; Nyquist Shannon theorem; Nyquist–Shannon–Kotelnikov sampling theorem; Whittaker–Shannon–Kotelnikov sampling theorem; Whittaker–Nyquist–Kotelnikov–Shannon sampling theorem; Nyquist-Shannon-Kotelnikov sampling theorem; Whittaker-Shannon-Kotelnikov sampling theorem; Whittaker-Nyquist-Kotelnikov-Shannon sampling theorem; Cardinal theorem of interpolation; WKS sampling theorem; Whittaker–Kotelnikow–Shannon sampling theorem; Whittaker-Kotelnikow-Shannon sampling theorem; Nyquist–Shannon–Kotelnikov; Whittaker–Shannon–Kotelnikov; Whittaker–Nyquist–Kotelnikov–Shannon; Nyquist-Shannon-Kotelnikov; Whittaker-Shannon-Kotelnikov; Whittaker-Nyquist-Kotelnikov-Shannon; Whittaker–Shannon sampling theorem; Whittaker–Nyquist–Shannon sampling theorem; Whittaker-Nyquist-Shannon sampling theorem; Whittaker-Shannon sampling theorem
теорема отсчетов (дискретазации)
Définition
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)
Wikipédia
Raygor readability estimate
The Raygor estimate graph is a readability metric for English text. It was developed by Alton L. Raygor, who published it in 1977.
The US grade level is calculated by the average number of sentences and letters per hundred words. These averages are plotted onto a specific graph where the intersection of the average number of sentences and the average number of letters/word determines the reading level of the content. Note that this graph is very similar to the Fry readability formula's graph.
This graph is primarily used in secondary education to help classify teaching materials and books into their appropriate reading groups.
