Sampling Algorithm

A Sampling Algorithm is a data processing algorithm that can solve a sampling task (select a random sample from a statistical population).

• AKA: Sampling Procedure.
• Context:
  • It can support a Sampling-based Algorithm.
• Example(s):
  • Consistent Weighted Sampling Algorithm,
  • Monte Carlo Sampling Algorithm,
  • Poisson Sampling Algorithm.
• See: Stochastic Approximate Bayesian Inference Algorithm, Hypothesis Evaluation Task.

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References

2009

• http://en.wikipedia.org/wiki/Sampling_(statistics)
  • Sampling is that part of statistical practice concerned with the selection of individual observations intended to yield some knowledge about a population of concern, especially for the purposes of statistical inference. Each observation measures one or more properties (weight, location, etc.) of an observable entity enumerated to distinguish objects or individuals. Survey weights often need to be applied to the data to adjust for the sample design. Results from probability theory and statistical theory are employed to guide practice. In business, sampling is widely used for gathering information about a population. [1]
• http://en.wikipedia.org/wiki/Sampling_(statistics)
  • • 4 Probability and nonprobability sampling
    • 5 Sampling methods

         o 5.1 Simple random sampling
         o 5.2 Systematic sampling
         o 5.3 Stratified sampling
         o 5.4 Probability proportional to size sampling
         o 5.5 Cluster sampling
         o 5.6 Matched random sampling
         o 5.7 Quota sampling
         o 5.8 Mechanical sampling
         o 5.9 Convenience sampling
         o 5.10 Line-intercept sampling
         o 5.11 Panel sampling
         o 5.12 Event Sampling Methodology

2006

• (Yves Tillé, 2006) ⇒ Yves Tillé. (2006). “Sampling Algorithms." Springer. ISBN:0387308148.
  • A sampling algorithm is a procedure used to select a sample. If the sampling design is known and if the population size is not too large, sa sample can be selected directly by enumerating all the samples as explained in Section 3.3. Nevertheless, when [math]\displaystyle{ N }[/math] is large, the number of possible samples becomes so large that it is practically impossible to enumerate all the samples. The objective of a sampling algorithm is to select a sample by avoiding the enumeration of all the samples. The main difficult of these implementation is thus the combinatory explosion of the number of possible samples.
  • Definition 31. A sampling algorithm is a procedure allowing the selection of a random sample.
  • Definition 32. A sampling algorithm is said to be enumerative if all the possible samples must be listed in order to select the random sample.
  • An efficient sampling algorithm is by definition a fast one. All enumerative algorithms are therefore inefficient.

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