• ☰
  • Home
  • Introduction
  • Generative models
  • Conditioning
  • Causal and statistical dependence
  • Conditional dependence
  • Social cognition
  • Interlude - Bayesian data analysis
  • Interlude - Algorithms for inference
  • Rational process models
  • Learning as conditional inference
  • Learning with a language of thought
  • Hierarchical models
  • Occam's Razor
  • Mixture models
  • Learning (deep) continuous functions
  • Appendix - JavaScript basics
  • Appendix - Useful distributions

1. Overview of Bayesian computational models

Read “How to Grow a Mind: Statistics, Structure, and Abstraction” by Joshua B. Tenenbaum, Charles Kemp, Thomas L. Griffiths, and Noah D. Goodman. This provides an overview of the approach taken by many of the models we’ll be looking at.

Reading questions:

a) How do Bayesian computational models differ from other kinds of reasoning systems you may be familiar with (formal logic, algebra, connectionism, deep learning, etc.)?

Extras

• Background in Psychology. Readers who have little background in psychology may find it useful to read How the Mind Works by Steven Pinker. Although it is getting on in years, it remains an excellent introduction to basic psychological phenomena and theory.

• Javascript. Experienced programmers who need an introduction to Javascript may find the appendix on Javascript sufficient. Students who have limited experience programming are encouraged to check out Introduction to JavaScript from Codeacademy (esp. Chapters 1-8).

• Background in Mathematics. This textbook requires a basic understanding of probability theory. The Khan Academy has a gentle introduction that should be sufficient for most students.