Complexity of finding embeddings in a k-tree
SIAM Journal on Algebraic and Discrete Methods
Network-based heuristics for constraint-satisfaction problems
Artificial Intelligence
A valuation-based language for expert systems
International Journal of Approximate Reasoning
Probabilistic inference in multiply connected belief networks using loop cutsets
International Journal of Approximate Reasoning
Topological parameters for time-space tradeoff
Artificial Intelligence
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Understanding the role of noise in stochastic local search: Analysis and experiments
Artificial Intelligence
Diagnosing faults in electrical power systems of spacecraft and aircraft
IAAI'08 Proceedings of the 20th national conference on Innovative applications of artificial intelligence - Volume 3
Understanding the scalability of Bayesian network inference using clique tree growth curves
Artificial Intelligence
Journal of Automated Reasoning
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In clique tree clustering, inference consists of propagation in a clique tree compiled from a Bayesian network. In this paper, we develop an analytical approach to characterizing clique tree growth as a function of increasing Bayesian network connectedness, specifically: (i) the expected number of moral edges in their moral graphs or (ii) the ratio of the number of non-root nodes to the number of root nodes. In experiments, we systematically increase the connectivity of bipartite Bayesian networks, and find that clique tree size growth is well-approximated by Gompertz growth curves. This research improves the understanding of the scaling behavior of clique tree clustering, provides a foundation for benchmarking and developing improved BN inference algorithms, and presents an aid for analytical trade-off studies of tree clustering using growth curves.