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Statistical Learning and Language Acquisition
This paper explores the why and what of statistical learning from a computational modelling perspective. We suggest that Bayesian techniques can be useful for understanding what kinds of learners and assumptions are necessary for successful statistical learning. The inferences that can be made by a learner are driven by both the units that such learning operates over and the levels of abstraction it includes. Other assumptions made by the learner have non-trivial affects as well, includingdoi:10.1515/9781934078242.383 fatcat:cz2s4amyirgbnar2a6vfw6zy4i