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  • John M. Winn, Charles M. Bishop
  • Mathematics, Computer Science
  • J. Mach. Learn. Res.
  • 2005 (First Publication: 1 December 2005)
  • Bayesian inference is now widely established as one of the principal foundations for machine learning. In practice, exact inference is rarely possible, and so a variety of approximation techniquesContinue Reading
  • Charles M. Bishop
  • Computer Science
  • 1999
  • One of the central issues in the use of principal component analysis (PCA) for data modelling is that of choosing the appropriate number of retained components. This problem was recently addressedContinue Reading
  • Charles M. Bishop
  • Computer Science
  • NIPS
  • 1998 (First Publication: 1 December 1998)
  • The technique of principal component analysis (PCA) has recently been expressed as the maximum likelihood solution for a generative latent variable model. In this paper we use this probabilisticContinue Reading