Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Deliberation scheduling for problem solving in time-constrained environments
Artificial Intelligence
A Language for Construction of Belief Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Refinement and coarsening of Bayesian networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Ideal reformulation of belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Abstraction in belief networks: the role of intermediate states in diagnostic reasoning
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
ACM Computing Surveys (CSUR)
Finding equilibria in large sequential games of imperfect information
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Lossless abstraction of imperfect information games
Journal of the ACM (JACM)
Indexing density models for incremental learning and anytime classification on data streams
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Harnessing the strengths of anytime algorithms for constant data streams
Data Mining and Knowledge Discovery
Anytime classification for a pool of instances
Machine Learning
A graph-theoretic analysis of information value
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
BT*: an advanced algorithm for anytime classification
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
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Despite the increasing popularity of Bayesian networks for representing and reasoning about uncertain situations, the complexity of inference in this formalism remains a significant concern. A viable approach to relieving the problem is trading off accuracy for computational efficiency. To this end, varying the granularity of state space of state variables appears to be a feasible strategy for controlling the evaluation process. We consider an anytime procedure for approximate evaluation of Bayesian networks based on this idea. On application to some simple networks, the procedure exhibits a smooth improvement in approximation quality as computation time increases. With the aim of developing principled control techniques, we also conduct a theoretical analysis of the quality of approximation. Our main result demonstrates that the error induced by state-space abstraction deceases with the distance from the abstracted nodes, where "distance" is defined in terms of d-separation. While the empirical results suggest that incremental state-space abstraction offers a viable performance profile, the theoretical results represent a starting point for the deliberation scheduling of our anytime approximation method.