- Bayesian modeling of human concept learning Joshua B. Tenenbaum
- Research Article Using Speakers' Referential
- Intuitive Theories as Grammars for Causal Inference Joshua B. Tenenbaum
- Structure learning in human causal induction Joshua B. Tenenbaum & Thomas L. Griffiths
- Learning the structure of similarity Joshua B. Tenenbaum
- Developmental Science 10:3 (2007), pp 307321 DOI: 10.1111/j.1467-7687.2007.00585.x 2007 The Authors. Journal compilation 2007 Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and
- Action Understanding as Inverse Planning Chris L. Baker, Rebecca Saxe & Joshua B. Tenenbaum
- september2006130 Statistics and the Bayesian mindStatistics and the Bayesian mind
- Learning Grounded Causal Models Noah D. Goodman (ndg@mit.edu), Vikash K. Mansinghka (vkm@mit.edu), Joshua B. Tenenbaum (jbt@mit.edu)
- Theory-based Social Goal Inference Chris L. Baker, Noah D. Goodman & Joshua B. Tenenbaum
- Running head: STRUCTURED STATISTICAL MODELS Structured statistical models of inductive reasoning
- Learnability of syntax 1 Running head: LEARNABILITY OF SYNTAX
- Cause and Intent: Social Reasoning in Causal Learning Noah D. Goodman, Chris L. Baker, Joshua B. Tenenbaum
- Structure learning in human causal induction Joshua B. Tenenbaum & Thomas L. Griffiths
- Bayesian modeling of human concept learning Joshua B. Tenenbaum
- AClass: An online algorithm for generative classification Vikash K. Mansinghka
- Theory Acquisition as Stochastic Search Tomer D. Ullman, Noah D. Goodman, Joshua B. Tenenbaum
- Learning Domain Structures Charles Kemp, Amy Perfors & Joshua B. Tenenbaum
- Graph approximations to geodesics on embedded manifolds
- The Role of Causal Models in Reasoning Under Uncertainty Tevye R. Krynski (tevye@mit.edu)
- Developmental Science 10:3 (2007), pp 281287 DOI: 10.1111/j.1467-7687.2007.00584.x 2007 The Authors. Journal compilation 2007 Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and
- Help or Hinder: Bayesian Models of Social Goal Inference
- Parametric Embedding for Class Visualization Tomoharu Iwata, Kazumi Saito, Naonori Ueda
- Learning Style Translation for the Lines of a Drawing WILLIAM T. FREEMAN, JOSHUA B. TENENBAUM, and EGON PASZTOR
- JOSHUA BRETT TENENBAUM Curriculum Vitae
- Exact and Approximate Sampling by Systematic Stochastic Search
- The Infinite Latent Events Model David Wingate, Noah D. Goodman, Daniel M. Roy and Joshua B. Tenenbaum
- Parametric Embedding for Class Visualization Tomoharu Iwata
- Word Learning as Bayesian Inference Department of Psychology
- From coincidences to discoveries 1 Running head: From coincidences to discoveries
- Two Proposals for Causal Grammars Thomas L. Griffiths
- Learning Systems of Concepts with an Infinite Relational Model Charles Kemp and Joshua B. Tenenbaum
- Nonsense and Sensibility: Inferring Unseen Possibilities Lauren A. Schmidt, Charles Kemp & Joshua B. Tenenbaum
- Learning Overhypotheses Charles Kemp, Amy Perfors & Joshua B. Tenenbaum
- Intuitive Theories of Mind: A Rational Approach to False Belief Noah D. Goodman1
- Context-Sensitive Induction Patrick Shafto1, Charles Kemp1, Elizabeth Baraff1, John D. Coley2, & Joshua B. Tenenbaum1
- Structure and strength in causal induction q Thomas L. Griffiths a,*, Joshua B. Tenenbaum b
- Cognitive Science 28 (2004) 303333 Children's causal inferences from indirect evidence
- Probability, algorithmic complexity, and subjective randomness Thomas L. Griffiths
- Bayesian models of inductive generalization Neville E. Sanjana & Joshua B. Tenenbaum
- 1. Introduction Consider the hypothetical case of a doctor trying to deter-
- Randomness and Coincidences: Reconciling Intuition and Probability Theory
- Rules and Similarity in Concept Learning Joshua B. Tenenbaum
- Mapping a manifold of perceptual observations Joshua B. Tenenbaum
- Everyday predictions 1 Running head: EVERYDAY PREDICTIONS
- Word Learning as Bayesian Inference: Evidence from Preschoolers fei@psych.ubc.ca
- Dynamical Causal Learning David Danks Thomas L. Griffiths
- Research Article Optimal Predictions in Everyday
- Topics in semantic representation 1 Running head: Topics in semantic representation
- JOSHUA BRETT TENENBAUM Curriculum Vitae
- A Rational Analysis of Rule-based Concept Learning Noah D. Goodman1 (ndg@mit.edu), Thomas Griffiths2 (tom griffiths@berkeley.edu),
- Learning Causal Schemata Charles Kemp, Noah D. Goodman & Joshua B. Tenenbaum
- Teacakes, Trains, Taxicabs and Toxins: A Bayesian Account of Predicting the Future
- Rules and Similarity in Concept Learning Joshua B. Tenenbaum
- Theory-Based Induction Charles Kemp (ckemp@mit.edu)
- Learning cross-cutting systems of categories Patrick Shafto, Charles Kemp, Vikash Mansinghka, Matthew Gordon, & Joshua B. Tenenbaum
- Research Article Secret Agents
- The Role of Causality in Judgment Under Uncertainty Tevye R. Krynski and Joshua B. Tenenbaum
- Learning style translation for the lines of a drawing An important task of pattern recognition is identifying or synthesizing stylistic variations
- Mapping a manifold of perceptual observations Joshua B. Tenenbaum
- Developmental Science 10:3 (2007), pp 288297 DOI: 10.1111/j.1467-7687.2007.00590.x 2007 The Authors. Journal compilation 2007 Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and