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*[[Pps sampling]] *[[Probability proportional to size sampling]] ...

...optimum allocation, is a method of sample size allocation in [[stratified sampling]] developed by [[Jerzy Neyman]] in 1934. This technique determines the opti In stratified sampling, the population is divided into ''L'' mutually exclusive and exhaustive str ...

{{Short description|Acceptance Sampling Technique}} In [[statistics]], a '''variables sampling plan''' is an [[acceptance sampling]] technique. Plans for [[Variable_(mathematics)|variables]] are intended fo ...

{{Short description|Work Sampling Technique}} ...l work flows. Other names used for it are 'activity sampling', 'occurrence sampling', and 'ratio delay study'.<ref name="Sheth" /> ...

...ematically optimal compromise between [[Simple random sample|simple random sampling]] and [[Offender profiling|strong profiling]] that most quickly finds a rar ...erage person to be a security risk, then when using [[square root]] biased sampling, people from the profiled group would be screened three times more often th ...

...ing [[inverse probability weighting]] to account for the difference in the sampling distribution between the collected data and the target population. The Horv |title = Model Assisted Survey Sampling ...

'''Non-uniform random variate generation''' or '''pseudo-random number sampling''' is the [[Numerical analysis|numerical]] practice of generating [[pseudo- ...[[John von Neumann]] in the early 1950s.<ref>{{cite book |chapter=Various Techniques Used in Connection with Random Digits |first=John |last=Von Neumann |author ...

{{Short description|Sampling technique}} ...x sampling methods.<ref>{{Cite book |last=Thompson |first=Steven K. |title=Sampling |date=2012 |publisher=John Wiley & Sons |isbn=978-1-118-16293-4 |edition=3r ...

...suitable for analyzing [[black box]] systems, and unlike the [[importance sampling]] method of [[variance reduction]], does not require detailed knowledge of The basic idea behind line sampling is to refine estimates obtained from the [[first-order reliability method]] ...

The '''demon algorithm''' is a [[Monte Carlo method]] for efficiently sampling members of a [[microcanonical ensemble]] with a given energy. An additional ...nally very expensive. [[Monte Carlo methods]] can overcome this problem by sampling microscopic states according to stochastic rules instead of modeling the co ...

...rl=http://www.cage.curtin.edu.au/mechanical/info/vibrations/tut3.htm|title=Sampling and Aliasing|publisher=Curtin University of Technology|access-date=2011-08- ...frequency is reduced. This limitation is called the [[Nyquist rate|Nyquist sampling rate]]. This introduces a [[Modular arithmetic|modulo operation]] onto the ...

Techniques used in digital signal analysis are extended to the analysis of [[electroen ...the highest frequency being detected). Modern EEG amplifiers use adequate sampling to resolve the EEG across the traditional medical band from DC to 70 or 100 ...

...lization of Thompson sampling.gif|thumb|350px|Concrete example of Thompson sampling applied to simulate treatment efficacy evaluation]] '''Thompson sampling''',<ref name="ref1"/><ref name="ref1b"/><ref name="FnTTutorial"/> named aft ...

...to assess [[species richness]] from the results of [[Sampling (statistics)|sampling]]. Rarefaction allows the calculation of species richness for a given numbe ...species that will be found. Rarefaction curves are created by randomly re-sampling the pool of ''N'' samples multiple times and then plotting the average numb ...

...observations – from a target [[probability distribution]] for which direct sampling is difficult. ...its dimension robustness, which makes it well-suited for high-dimensional sampling problems. The pCN algorithm is well-defined, with non-degenerate acceptanc ...

...ty|last2=Pedroni|first2=N|s2cid=9845287|chapter=Subset simulation and line sampling for advanced Monte Carlo reliability analysis|year=2009|doi=10.1201/9780203 ...f>{{cite journal|last1=Botev|first1=Z. I. |last2=L’Ecuyer|first2=P.|title= Sampling Conditionally on a Rare Event via Generalized Splitting | journal=INFORMS J ...

...s of the material. Stereology is a method that utilizes random, systematic sampling to provide unbiased and quantitative data. It is an important and efficien ...nsional probes (e.g. needle biopsy), projected images, and other kinds of 'sampling'. It is especially useful when the sample has a lower spatial dimension tha ...

...m''', is a generalization of the [[Nyquist–Shannon sampling theorem]] for sampling one-dimensional [[band-limited]] functions to higher-dimensional [[Euclidea ...ized model but only an approximation for real-world functions and sampling techniques, albeit in practice often a very good one. ...

...'(2), 1-40.</ref> After model estimation, good-of-fit testing, through the sampling of random networks from the fitted model, should be performed to ensure tha ...statistics)|random sampling]] or [[snowball sampling]] [[data collection]] techniques.<ref>Stivala, A. D., Gallagher, H. C., Rolls, D. A., Wang, P., & Robins, G. ...

{{Short description|Statistical sampling techniques}} ...classes/categories represented). These terms are used both in statistical sampling, survey design methodology and in [[machine learning]]. ...