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Strong and weak sampling

From Wikipedia, the free encyclopedia

Strong and weak sampling are two sampling approach[1] in Statistics, and are popular in computational cognitive science and language learning.[2] In strong sampling, it is assumed that the data are intentionally generated as positive examples of a concept,[3] while in weak sampling, it is assumed that the data are generated without any restrictions.[4]

Formal Definition

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

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

Consequence: Posterior computation under Weak Sampling

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Therefore the likelihood for all hypotheses will be "ignored".

References

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  1. ^ Xu, Fei. "Bayesian word learning Sensitivity to sampling in Bayesian word learning" (PDF). MIT. Developmental Science.
  2. ^ Hsu, Anne. "Sampling assumptions in language learning 1 Running head: SAMPLING ASSUMPTIONS IN LANGUAGE LEARNING Sampling assumptions affect use of indirect negative evidence in language learning". ResearchGate.
  3. ^ Navarro, Danielle. "Lecture 20: Strong vs weak sampling" (PDF). Computational Cognitive Science.
  4. ^ Navarro, Daniel (2012). "Sampling assumptions in inductive generalization". Cognitive Science. 36 (2): 187–223. doi:10.1111/j.1551-6709.2011.01212.x. PMID 22141440.
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