The Indian buffet process: An introduction and review

Thomas L. Griffiths, Zoubin Ghahramani

Research output: Contribution to journalArticlepeer-review

275 Scopus citations


The Indian buffet process is a stochastic process defining a probability distribution over equivalence classes of sparse binary matrices with a finite number of rows and an unbounded number of columns. This distribution is suitable for use as a prior in probabilistic models that represent objects using a potentially infinite array of features, or that involve bipartite graphs in which the size of at least one class of nodes is unknown. We give a detailed derivation of this distribution, and illustrate its use as a prior in an infinite latent feature model. We then review recent applications of the Indian buffet process in machine learning, discuss its extensions, and summarize its connections to other stochastic processes.

Original languageEnglish (US)
Pages (from-to)1185-1224
Number of pages40
JournalJournal of Machine Learning Research
StatePublished - Apr 2011
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Control and Systems Engineering
  • Statistics and Probability


  • Beta process
  • Chinese restaurant processes
  • Exchangeable distributions
  • Latent variable models
  • Markov chain Monte Carlo
  • Nonparametric Bayes
  • Sparse binary matrices


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