A sufficiently fast algorithm for finding close to optimal clique trees
Artificial Intelligence
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Optimizing exact genetic linkage computations
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
A complete anytime algorithm for treewidth
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Belief Update in Bayesian Networks Using Uncertain Evidence
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Performance Evaluation of Algorithms for Soft Evidential Update in Bayesian Networks: First Results
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
Best-first search for treewidth
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
A practical algorithm for finding optimal triangulations
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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It is well-known that the observation of a variable in a Bay-esian network can affect the effective connectivity of the network, which in turn affects the efficiency of inference. Unfortunately, the observed variables may not be known until runtime, which limits the amount of compile-time optimization that can be done in this regard. In this paper, we consider how to improve inference when we know the likelihood of a variable being observed. We show how these probabilities of observation can be exploited to improve existing heuristics for choosing elimination orderings for inference. Empirical tests over a set of benchmark networks using the Variable Elimination algorithm show reductions of up to 50%, 70%, and 55% in multiplications, summations, and runtime, respectively.