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Approximate Separability for Weak Interaction in Dynamic Systems

Machine Learning 2012-07-02 v1 Artificial Intelligence Machine Learning

Abstract

One approach to monitoring a dynamic system relies on decomposition of the system into weakly interacting subsystems. An earlier paper introduced a notion of weak interaction called separability, and showed that it leads to exact propagation of marginals for prediction. This paper addresses two questions left open by the earlier paper: can we define a notion of approximate separability that occurs naturally in practice, and do separability and approximate separability lead to accurate monitoring? The answer to both questions is afirmative. The paper also analyzes the structure of approximately separable decompositions, and provides some explanation as to why these models perform well.

Keywords

Cite

@article{arxiv.1206.6846,
  title  = {Approximate Separability for Weak Interaction in Dynamic Systems},
  author = {Avi Pfeffer},
  journal= {arXiv preprint arXiv:1206.6846},
  year   = {2012}
}

Comments

Appears in Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI2006)

R2 v1 2026-06-21T21:27:46.479Z