Results 211 to 220 of about 481,341 (267)

Microprogramming for probability distribution sampling

Proceedings of the ACM annual conference on - ACM'72, 1972
Microprogramming of special instructions for sampling of random variates from any probability distribution is a means of increasing sampling speed. The diversity of sampling techniques is narrowed to one general algorithm; conditional bit sampling.
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On inverse sampling with unequal probabilities

Biometrika, 1964
SUMMARY Sampford (1962) has considered the following sampling scheme to select a sample with n distinct population units. Sampling with unequal probabilities with replacement is carried out until (n + 1) different population units are selected, the last unit is not recorded in the sample to insure some simplicity in the estimation procedure. The method
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A Probability Sample of Gay Males

Journal of Homosexuality, 1990
Data are presented from a national probability sample of males interviewed by telephone and asked their sexual orientation. Of these males 3.7 percent reported that they were homosexual or bisexual. Homosexual/bisexual men were compared with heterosexual ones on the demographic variables. This sample produced larger numbers in those groups which appear
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Population Sampling: Probability and Non-Probability Techniques

Prehospital and Disaster Medicine, 2023
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Sampling: bridging probability and non-probability designs

International Journal of Social Research Methodology, 2013
This article reconceptualizes sampling in social research. It is argued that three inter-related a priori assumptions limit on the possibility of sample design, namely: (a) the ontology of the case, (b) the epistemological assumptions underpinning what properties are necessary to know the case and (c) the logistics involved in the process of ‘casing ...
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Comparing inclusion probabilities and drawing probabilities for rejective sampling and successive sampling

Statistics & Probability Letters, 1992
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Uncertainty in Sampling Designs for Non-probability Samples

Non-probability samples involve some form of arbitrary selection of units into the sample, and, as a matter of fact, inclusion probabilities are unknown. Hence, it is not possible to apply probability randomization theory to make inference about the finite population parameters.
Pier Luigi Conti, Daniela Marella
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Probability and Non Probability Sampling

Asian Research Journal of Business Management, 2017
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Computational advantage of quantum random sampling

Reviews of Modern Physics, 2023
Dominik Hangleiter, Jens Eisert
exaly  

Probability Sampling

2022
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