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[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
In strong sampling, we assume observation is randomly sampled from the true hypothesis:
In weak sampling, we assume observations randomly sampled and then classified:
References
- ^ Xu, Fei. "Bayesian word learning Sensitivity to sampling in Bayesian word learning" (PDF). MIT. Developmental Science.
- ^ 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.
- ^ Navarro, Danielle. "Lecture 20: Strong vs weak sampling" (PDF). Computational Cognitive Science.
- ^ Navarro, Daniel. "Sampling assumptions in inductive generalization". Cognitive science. 36 (2): 187-223. doi:10.1111/j.1551-6709.2011.01212.x. PMID 22141440.