An Objective Function for Belief Net Triangulation

Marina Meilă, Michael I. Jordan
Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, PMLR R1:355-362, 1997.

Abstract

This paper presents a new approach to the triangulation of belief networks. Triangulation is a combinatorial optimization problem; our idea is to embed its discrete domain into a continuous domain e. Then, by suitably extending the objective function over e, we can make use of continuous optimization techniques to do the minimization. We used an upper bound of the total junction tree weight as the cost function. The appropriateness of this choice is discussed and explored by simulations.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR1-meila97a, title = {An Objective Function for Belief Net Triangulation}, author = {Meil\u{a}, Marina and Jordan, Michael I.}, booktitle = {Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics}, pages = {355--362}, year = {1997}, editor = {Madigan, David and Smyth, Padhraic}, volume = {R1}, series = {Proceedings of Machine Learning Research}, month = {04--07 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r1/meila97a/meila97a.pdf}, url = {https://proceedings.mlr.press/r1/meila97a.html}, abstract = {This paper presents a new approach to the triangulation of belief networks. Triangulation is a combinatorial optimization problem; our idea is to embed its discrete domain into a continuous domain e. Then, by suitably extending the objective function over e, we can make use of continuous optimization techniques to do the minimization. We used an upper bound of the total junction tree weight as the cost function. The appropriateness of this choice is discussed and explored by simulations.}, note = {Reissued by PMLR on 30 March 2021.} }
Endnote
%0 Conference Paper %T An Objective Function for Belief Net Triangulation %A Marina Meilă %A Michael I. Jordan %B Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1997 %E David Madigan %E Padhraic Smyth %F pmlr-vR1-meila97a %I PMLR %P 355--362 %U https://proceedings.mlr.press/r1/meila97a.html %V R1 %X This paper presents a new approach to the triangulation of belief networks. Triangulation is a combinatorial optimization problem; our idea is to embed its discrete domain into a continuous domain e. Then, by suitably extending the objective function over e, we can make use of continuous optimization techniques to do the minimization. We used an upper bound of the total junction tree weight as the cost function. The appropriateness of this choice is discussed and explored by simulations. %Z Reissued by PMLR on 30 March 2021.
APA
Meilă, M. & Jordan, M.I.. (1997). An Objective Function for Belief Net Triangulation. Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R1:355-362 Available from https://proceedings.mlr.press/r1/meila97a.html. Reissued by PMLR on 30 March 2021.

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