Propositionalizing the EM algorithm by BDDs

  title={Propositionalizing the EM algorithm by BDDs},
  author={Taisuke Sato},
We propose an Expectation-Maximization (EM) algorithm which works on binary decision diagrams (BDDs). The proposed algorithm, BDD-EM algorithm, opens a way to apply BDDs to statistical learning. The BDD-EM algorithm makes it possible to learn probabilities in statistical models described by Boolean formulas, and the time complexity is proportional to the size of BDDs representing them. We apply the BDD-EM algorithm to prediction of intermittent errors in logic circuits and demonstrate that it… CONTINUE READING
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Publications referenced by this paper.
Showing 1-10 of 10 references

Graph-Based Algorithms for Boolean Function Manipulation

IEEE Transactions on Computers • 1986
View 11 Excerpts
Highly Influenced

Binary Decision Diagrams

IEEE Transactions on Computers • 1978
View 7 Excerpts
Highly Influenced

Propositionalizing the EM algorithm by BDDs

M. Ishihata, Y. Kameya, T. Sato, S. Minato
Technical Report TR08-0004, Dept. of Computer Science, Tokyo Institute of Technology • 2008
View 2 Excerpts

Binary decision diagrams in theory and practice

International Journal on Software Tools for Technology Transfer • 2001
View 2 Excerpts

A Simple Model for Sequences of Relational State Descriptions , in Proceedings of the

I. Thon, N. Landwehr, L. De Raedt

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