Variational Bayes via propositionalized probability computation in PRISM

@article{Sato2008VariationalBV,
  title={Variational Bayes via propositionalized probability computation in PRISM},
  author={Taisuke Sato and Yoshitaka Kameya and Kenichi Kurihara},
  journal={Annals of Mathematics and Artificial Intelligence},
  year={2008},
  volume={54},
  pages={135-158}
}
We propose a logic-based approach to variational Bayes (VB) via propositionalized probability computation in a symbolic-statistical modeling language PRISM. PRISM computes probabilities of logical formulas by reducing them to AND/OR boolean formulas called explanation graphs containing probabilistic ${\tt msw/2}$ atoms. We put Dirichlet priors on parameters of ${\tt msw/2}$ atoms and derive a variational Bayes EM algorithm that learns their hyper parameters from data. It runs on explanation… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-10 of 36 references

Ensemble learning for hidden Markov models

D. MacKay
Technical report, Cavendish Laboratory, University of Cambridge • 1997
View 11 Excerpts
Highly Influenced

Bayesian Classification (AutoClass): Theory and Results

Advances in Knowledge Discovery and Data Mining • 1996
View 13 Excerpts
Highly Influenced

Language modeling with probabilistic left corner parsing

Computer Speech & Language • 2005
View 4 Excerpts
Highly Influenced

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