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Alan Yuille [6]Alan L. Yuille [4]A. Yuille [2]
  1. Probabilistic Models of Cognition: Conceptual Foundations.Nick Chater & Alan Yuille - 2006 - Trends in Cognitive Sciences 10 (7):287-291.
    Remarkable progress in the mathematics and computer science of probability has led to a revolution in the scope of probabilistic models. In particular, ‘sophisticated’ probabilistic methods apply to structured relational systems such as graphs and grammars, of immediate relevance to the cognitive sciences. This Special Issue outlines progress in this rapidly developing field, which provides a potentially unifying perspective across a wide range of domains and levels of explanation. Here, we introduce the historical and conceptual foundations of the approach, explore (...)
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  2.  77
    Vision as Bayesian Inference: Analysis by Synthesis?Alan Yuille & Daniel Kersten - 2006 - Trends in Cognitive Sciences 10 (7):301-308.
  3.  32
    Bayesian Generic Priors for Causal Learning.Hongjing Lu, Alan L. Yuille, Mimi Liljeholm, Patricia W. Cheng & Keith J. Holyoak - 2008 - Psychological Review 115 (4):955-984.
  4. Probabilistic Models of Cognition. Special Issue.N. Chater, J. Tenenbaum & A. Yuille - forthcoming - Trends in Cognitive Sciences.
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  5.  50
    Probabilistic Models of Cognition: Where Next?Nick Chater, Joshua B. Tenenbaum & Alan Yuille - 2006 - Trends in Cognitive Sciences 10 (7):292-293.
  6.  28
    A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.Hongjing Lu, Randall R. Rojas, Tom Beckers & Alan L. Yuille - 2016 - Cognitive Science 40 (2):404-439.
    Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that (...)
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  7. Probabilistic Models of Cognition: Where Next.N. Carter, J. B. Tenenbaum & A. Yuille - 2006 - Trends in Cognitive Sciences 10 (7):292-293.
     
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  8. Multilevel Enhancement and Detection of Stereo Disparity Surfaces.Yibing Yang & Alan L. Yuille - 1995 - Artificial Intelligence 78 (1-2):121-145.
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  9.  24
    A Primer on Probabilistic Inference.Thomas L. Griffiths & Alan Yuille - 2008 - In Nick Chater & Mike Oaksford (eds.), The Probabilistic Mind: Prospects for Bayesian Cognitive Science. Oxford University Press. pp. 33--57.
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    Data Driven Markov Chain Monte Carlo Algorithm.Alan Yuille & Daniel Kersten - 2006 - Trends in Cognitive Sciences 10 (7):301-308.
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  11.  27
    Subjective Probability in a Nutshell.Nick Chater, Joshua B. Tenenbaum & Alan Yuille - 2006 - Trends in Cognitive Sciences 10 (7):287-291.
  12. Winner-Take-All Mechanisms.Alan L. Yuille & Davi Geiger - 1995 - In Michael A. Arbib (ed.), Handbook of Brain Theory and Neural Networks. MIT Press. pp. 1--1056.