A tutorial introduction to Bayesian models of cognitive development

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by Amy Perfors , Joshua B. Tenenbaum , Thomas L. Griffiths , Fei Xu
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5 Technical Introduction: A Primer on Probabilistic Inference – Thomas L. Griffiths, Alan Yuille - 2006
5 Open problems in universal induction & intelligence – Marcus Hutter - 2009
Implementation of Gaussian Process models for . . . – Keith Russell Thompson - 2009
6 Bayesian Input Variable Selection Using Posterior Probabilities and Expected Utilities – Aki Vehtari, Jouko Lampinen - 2002
25 Language Evolution by Iterated Learning With Bayesian Agents – Thomas L. Griffiths , Michael L. Kalish - 2007
2 Machine Learning: A Probabilistic Approach – David Barber - 2006
CONTENTS From Homo Sapiens to the Renaissance The Renaissance – Ralph F. Miles, Early Analysis Of Games
Preprint of the Book Chapter: “Bayesian Versus Frequentist Inference” – Eric-jan Wagenmakers, Michael Lee, Tom Lodewyckx, Geoff Iverson
7 Natively probabilistic computing – Vikash Kumar Mansinghka, Joshua B. Tenenbaum - 2009
23 Bayesian models of cognition – Thomas L. Griffiths, Charles Kemp, Joshua B. Tenenbaum
564 Dynamic Bayesian Networks: Representation, Inference and Learning – Kevin Patrick Murphy - 2002
49 Ensemble learning for independent component analysis – James W. Miskin - 2000
2 Representing Stimulus Similarity – Daniel J. Navarro - 2002
2 Towards a Cognitive Linguistic Approach to Language Comprehension – G. Scott Mcglashan, Scott Mcglashan - 1992
Cognition – Jacob Feldman
Artificial Neural Network Modeling of Forest Tree Growth – Christopher Gordon, Christopher Gordon, Christopher Gordon - 1998
13 Bayesian Methods for Neural Networks: Theory and Applications – David J.C. MacKay, Cavendish Laboratory - 1995
21 A Bayesian Framework for Concept Learning – Joshua B. Tenenbaum - 1999
Solving the Probabilistic Decoding Problems Using Evolutionary Computation Techniques – Mahmoud A. Ismail, Reda A. El-khoribi