Variational algorithms for approximate Bayesian inference

@inproceedings{Beal2003VariationalAF,
  title={Variational algorithms for approximate Bayesian inference},
  author={Matthew J. Beal},
  year={2003}
}
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. Unfortunately the computations required are usually intractable. This thesis presents a unified variational Bayesian (VB) framework which approximates these computations in models with latent variables using a lower bound on the marginal likelihood. Chapter 1 presents background… CONTINUE READING

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