Approximations of unsupervised Bayes learning procedures

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Abstract

Computational constrains often limit the practical applicability of coherent Bayes solutions to unsupervised sequential learning problems. These problems arise when attempts are made to learn about parameters on the basic of unclassified observations, each stemming from any one of k classes (k≥2). In this paper, the difficulties of the Bayes procedure will be discussed and existing approximate learning procedures will be reviewed for broad types of problems involving mixtures of probability densities. In particular a quasi-Bayes approximate learning procedure will be motivated and defined and its convergence properties will be reported for several special cases.

Original languageEnglish
Pages (from-to)69-81
Number of pages13
JournalTrabajos de Estadistica Y de Investigacion Operativa
Volume31
Issue number1
DOIs
StatePublished - Feb 1980
Externally publishedYes

Keywords

  • Approximation
  • Bayesian
  • Classification
  • Estimation
  • Identifiable Mixtures
  • Quasi-bayes
  • Sequential
  • Stochastic

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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