A Tutorial Introduction to Bayesian Models of Cognitive Development

Abstract

We present an introduction to Bayesian inference as it is used in probabilistic models of cognitive development. Our goal is to provide an intuitive and accessible guide to the what, the how, and the why of the Bayesian approach: what sorts of problems and data the framework is most relevant for, and how and why it may be useful for developmentalists. We emphasize a qualitative understanding of Bayesian inference, but also include information about additional resources for those interested in the cognitive science applications, mathematical foundations or machine learning details in more depth. In addition, we discuss some important interpretation issues that often arise when evaluating Bayesian models in cognitive science.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2011
Accession Number
ADA537429

Entities

People

  • Amy Perfors
  • Fei Xu
  • Joshua B. Tenenbaum
  • Thomas L. Griffiths

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Bayesian Inference
  • Bayesian Networks
  • Causal Reasoning
  • Cognition
  • Cognitive Science
  • Computational Science
  • Information Processing
  • Information Science
  • Information Systems
  • Machine Learning
  • Monte Carlo Method
  • Neural Networks
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Psychology

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Theoretical Analysis.

Technology Areas

  • AI & ML