Guest editors' introduction to the special issue on bayesian nonparametrics

Ryan P. Adams, Emily B. Fox, Erik B. Sudderth, Yee Whye Teh

Research output: Contribution to journalReview article

2 Scopus citations

Abstract

The articles in this special issue discuss the applications supported by Bayesian nonparametric modeling. These probabilistic models defined over infinite-dimensional parameter spaces. For Gaussian process models of regression and classification functions, the parameter space consists of a set of continuous functions. For the Dirichlet process mixture models used in density estimation and clustering, the parameter space is dense in the space of probability measures. Bayesian nonparametric models provide a flexible framework for modeling complex data and a promising alternative to classical model selection methods. Due to recent computational advances, these approaches have received increasing attention in machine learning, statistics, probability, and related application domains.

Original languageEnglish (US)
Article number7004120
Pages (from-to)209-211
Number of pages3
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume37
Issue number2
DOIs
StatePublished - Feb 1 2015
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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