Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Graph minors. IV. Tree-width and well-quasi-ordering
Journal of Combinatorial Theory Series B
Graph minors: X. obstructions to tree-decomposition
Journal of Combinatorial Theory Series B
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Chaff: engineering an efficient SAT solver
Proceedings of the 38th annual Design Automation Conference
Optimizing exact genetic linkage computations
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Hybrid Processing of Beliefs and Constraints
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Constraint Processing
Optimal time-space tradeoff in probabilistic inference
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Mixtures of deterministic-probabilistic networks and their AND/OR search space
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Case-factor diagrams for structured probabilistic modeling
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
AND/OR search spaces for graphical models
Artificial Intelligence
Case-factor diagrams for structured probabilistic modeling
Journal of Computer and System Sciences
RC_Link: Genetic linkage analysis using Bayesian networks
International Journal of Approximate Reasoning
On Directed and Undirected Propagation Algorithms for Bayesian Networks
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Mixed deterministic and probabilistic networks
Annals of Mathematics and Artificial Intelligence
Sound and efficient inference with probabilistic and deterministic dependencies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
AND/OR Branch-and-Bound search for combinatorial optimization in graphical models
Artificial Intelligence
Memory intensive AND/OR search for combinatorial optimization in graphical models
Artificial Intelligence
Performing Bayesian inference by weighted model counting
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
AND/OR multi-valued decision diagrams (AOMDDs) for graphical models
Journal of Artificial Intelligence Research
Recognizing activities with multiple cues
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
The independent choice logic and beyond
Probabilistic inductive logic programming
On the structure of elimination trees for Bayesian network inference
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Tuffy: scaling up statistical inference in Markov logic networks using an RDBMS
Proceedings of the VLDB Endowment
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Recursive Conditioning (RC) was introduced recently as an any-space algorithm for inference in Bayesian networks which can trade time for space by varying the size of its cache at the increment needed to store a floating point number. Under full caching, RC has an asymptotic time and space complexity which is comparable to mainstream algorithms based on variable elimination and clustering (exponential in the network treewidth and linear in its size). We show two main results about RC in this paper. First, we show that its actual space requirements under full caching are much more modest than those needed by mainstream methods and study the implications of this finding. Second, we show that RC can effectively deal with determinism in Bayesian networks by employing standard logical techniques, such as unit resolution, allowing a significant reduction in its time requirements in certain cases. We illustrate our results using a number of benchmark networks, including the very challenging ones that arise in genetic linkage analysis.