exhaustive sampling - translation to ρωσικά
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exhaustive sampling - translation to ρωσικά

SET OF EVENTS WHOSE UNION COVERS THE ENTIRE SAMPLE SPACE
Collectively exhaustive; Collective Exhaustion; Collective exhaustion; Collectively Exhaustive Events; Jointly exhaustive; Jointly Exhaustive

exhaustive sampling      
исчерпывающий отбор
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         
  • the sampled sequences are identical}}, even though the original continuous pre-sampled functions are not. If these were audio signals, <math>x(t)</math> and <math>x_A(t)</math> might not sound the same. But their samples (taken at rate ''f''<sub>s</sub>) are identical and would lead to identical reproduced sounds; thus ''x''<sub>A</sub>(''t'') is an alias of ''x''(''t'') at this sample rate.
  • The samples of two sine waves can be identical when at least one of them is at a frequency above half the sample rate.
  • A family of sinusoids at the critical frequency, all having the same sample sequences of alternating +1 and –1. That is, they all are aliases of each other, even though their frequency is not above half the sample rate.
  • Properly sampled image
  • Subsampled image showing a [[Moiré pattern]]
  • The figure on the left shows a function (in gray/black) being sampled and reconstructed (in gold) at steadily increasing sample-densities, while the figure on the right shows the frequency spectrum of the gray/black function, which does not change. The highest frequency in the spectrum is ½ the width of the entire spectrum. The width of the steadily-increasing pink shading is equal to the sample-rate. When it encompasses the entire frequency spectrum it is twice as large as the highest frequency, and that is when the reconstructed waveform matches the sampled one.
  • Spectrum, ''X<sub>s</sub>''(''f''), of a properly sampled bandlimited signal (blue) and the adjacent DTFT images (green) that do not overlap. A ''brick-wall'' low-pass filter, ''H''(''f''), removes the images, leaves the original spectrum, ''X''(''f''), and recovers the original signal from its samples.
  • 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)

Ορισμός

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)

Βικιπαίδεια

Collectively exhaustive events

In probability theory and logic, a set of events is jointly or collectively exhaustive if at least one of the events must occur. For example, when rolling a six-sided die, the events 1, 2, 3, 4, 5, and 6 balls of a single outcome are collectively exhaustive, because they encompass the entire range of possible outcomes.

Another way to describe collectively exhaustive events is that their union must cover all the events within the entire sample space. For example, events A and B are said to be collectively exhaustive if

A B = S {\displaystyle A\cup B=S}

where S is the sample space.

Compare this to the concept of a set of mutually exclusive events. In such a set no more than one event can occur at a given time. (In some forms of mutual exclusion only one event can ever occur.) The set of all possible die rolls is both mutually exclusive and collectively exhaustive (i.e., "MECE"). The events 1 and 6 are mutually exclusive but not collectively exhaustive. The events "even" (2,4 or 6) and "not-6" (1,2,3,4, or 5) are also collectively exhaustive but not mutually exclusive. In some forms of mutual exclusion only one event can ever occur, whether collectively exhaustive or not. For example, tossing a particular biscuit for a group of several dogs cannot be repeated, no matter which dog snaps it up.

One example of an event that is both collectively exhaustive and mutually exclusive is tossing a coin. The outcome must be either heads or tails, or p (heads or tails) = 1, so the outcomes are collectively exhaustive. When heads occurs, tails can't occur, or p (heads and tails) = 0, so the outcomes are also mutually exclusive.

Another example of events being collectively exhaustive and mutually exclusive at same time are, event "even" (2,4 or 6) and event "odd" (1,3 or 5) in a random experiment of rolling a six-sided die. These both events are mutually exclusive because even and odd outcome can never occur at same time. The union of both "even" and "odd" events give sample space of rolling the die, hence are collectively exhaustive.

Μετάφραση του &#39exhaustive sampling&#39 σε Ρωσικά