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Strong and weak sampling

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Comment: Requires significant coverage in multiple independent reliable secondary sources. Dan arndt (talk) 02:18, 14 December 2020 (UTC)

Strong and weak sampling are two sampling approach in Statistics, and are popular in computational cognitive science. In strong sampling, it is assumed that the data are intentionally generated as positive examples of a concept, while in weak sampling, it is assumed that the data are generated without any restrictions.^[1]

Formal Definition

In strong sampling, we assume observation is randomly sampled from the true hypothesis:

$P(x|h)={\begin{cases}{\frac {1}{|h|}}{\text{, if }}x\in h\\0{\text{, otherwise}}\end{cases}}$

In weak sampling, we assume observations randomly sampled and then classified:

$P(x|h)={\begin{cases}1{\text{, if }}x\in h\\0{\text{, otherwise}}\end{cases}}$

References

^ Navarro, Daniel. "Sampling assumptions in inductive generalization". Cognitive science. 36 (2): 187-223. doi:10.1111/j.1551-6709.2011.01212.x. PMID 22141440.

External Link

Lecture 20: Strong vs weak sampling

Category:Statistics

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