Variational Bayes via propositionalized probability computation in PRISM

  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},
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


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