Corpus ID: 11861569

Variational algorithms for approximate Bayesian inference

@inproceedings{Beal2003VariationalAF,
  title={Variational algorithms for approximate Bayesian inference},
  author={M. Beal},
  year={2003}
}
  • M. Beal
  • Published 2003
  • Computer Science
  • The Bayesian framework for machine learning allows for the incorporation of prior knowledge in a coherent way, avoids overfitting problems, and provides a principled basis for selecting between alternative models. [...] Key Method Chapter 2 forms the theoretical core of the thesis, generalising the expectationmaximisation (EM) algorithm for learning maximum likelihood parameters to the VB EM algorithm which integrates over model parameters. The algorithm is then specialised to the large family of conjugate…Expand Abstract
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