Encyclopedia of the Sciences of Learning

2012 Edition
| Editors: Norbert M. Seel

Bayesian Learning

Reference work entry
DOI: https://doi.org/10.1007/978-1-4419-1428-6_255


Theoretical Background

Bayesian methods have undergone tremendous progress in recent years, due largely to mathematical advances in probability and estimation theory (Chater et al. 2006). These advances have allowed theorists to express and derive predictions from far more sophisticated models than previously possible. These models have generated a good deal of excitement for at least two reasons. First, they offer a new interpretation of the goals of cognitive systems, in terms of inductive probabilistic inference, which has revived attempts at rational explanation of human behavior (Oaksford and Chater 2007). Second, Bayesian models may have the potential to explain some of the most complex aspects of human cognition, such as language acquisition or reasoning under uncertainty, where structured information and incomplete knowledge combine in a way that has defied previous approaches (e.g., Kemp and Tenenbaum 2008)....

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of PsychologyThe University of Texas at AustinAustinUSA
  2. 2.University of ColoradoBoulderUSA