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1
Casual Models and Conditional Reasoning
In: The Oxford handbook of causal reasoning (2017), S. 327-346
Leibniz-Zentrum Allgemeine Sprachwissenschaft
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2
Identification of Probabilities ...
Vitanyi, Paul M. B.; Chater, Nick. - : arXiv, 2017
Abstract: Within psychology, neuroscience and artificial intelligence, there has been increasing interest in the proposal that the brain builds probabilistic models of sensory and linguistic input: that is, to infer a probabilistic model from a sample. The practical problems of such inference are substantial: the brain has limited data and restricted computational resources. But there is a more fundamental question: is the problem of inferring a probabilistic model from a sample possible even in principle? We explore this question and find some surprisingly positive and general results. First, for a broad class of probability distributions characterised by computability restrictions, we specify a learning algorithm that will almost surely identify a probability distribution in the limit given a finite i.i.d. sample of sufficient but unknown length. This is similarly shown to hold for sequences generated by a broad class of Markov chains, subject to computability assumptions. The technical tool is the strong law of ... : 31 pages LaTeX. arXiv admin note: substantial text overlap with arXiv:1311.7385 ...
Keyword: Artificial Intelligence cs.AI; FOS Computer and information sciences; Machine Learning cs.LG
URL: https://dx.doi.org/10.48550/arxiv.1708.01611
https://arxiv.org/abs/1708.01611
BASE
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3
Identification of probabilities
Vitányi, Paul M.B.; Chater, Nick. - : Academic Press, 2017
BASE
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4
Identification of probabilities
Vitanyi, Paul M. B.; Chater, Nick. - : Elsevier, 2017
BASE
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