Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic inference and influence diagrams
Operations Research
A graph-based inference method for conditional independence
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Decision making using probabilistic inference methods
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
d-Separation: From Theorems to Algorithms
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Decision-theoretic foundations for causal reasoning
Journal of Artificial Intelligence Research
Computational advantages of relevance reasoning in Bayesian belief networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Identifying independencies in causal graphs with feedback
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
A graph-theoretic analysis of information value
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Graphical Models as Languages for Computer Assisted Diagnosis and Decision Making
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Decomposition of Influence Diagrams
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Optimal structure identification with greedy search
The Journal of Machine Learning Research
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - New trends in probabilistic graphical models
Belief updating in Bayesian networks by using a criterion of minimum time
Pattern Recognition Letters
Dynamic multiagent probabilistic inference
International Journal of Approximate Reasoning
Inference in qualitative probabilistic networks revisited
International Journal of Approximate Reasoning
An Inductive Logic Programming Approach to Statistical Relational Learning
Proceedings of the 2005 conference on An Inductive Logic Programming Approach to Statistical Relational Learning
Multi-agent influence diagrams for representing and solving games
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Solving linear-quadratic conditional Gaussian influence diagrams
International Journal of Approximate Reasoning
A graphical modeling approach to simplifying sequential teams
WiOPT'09 Proceedings of the 7th international conference on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks
Sequential team form and its simplification using graphical models
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
GRN model of probabilistic databases: construction, transition and querying
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Variable elimination for influence diagrams with super value nodes
International Journal of Approximate Reasoning
Maximum-likelihood sequence detector for dynamic mode high density probe storage
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Maximum-likelihood sequence detector for dynamic mode high density probe storage
IEEE Transactions on Communications
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
Sequential decision making with partially ordered preferences
Artificial Intelligence
Welldefined decision scenarios
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Efficient value of information computation
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Unconstrained influence diagrams
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Probabilistic models for agents' beliefs and decisions
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Evaluating influence diagrams using LIMIDs
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Pivotal pruning of trade-offs in QPNs
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
EKAW'06 Proceedings of the 15th international conference on Managing Knowledge in a World of Networks
Exploiting dynamic independence in a static conditioning graph
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
Structured probabilistic inference
International Journal of Approximate Reasoning
Distributed data association in smart camera networks using belief propagation
ACM Transactions on Sensor Networks (TOSN)
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One of the benefits of belief networks and influence diagrams is that so much knowledge is captured in the graphical structure. In particular, statements of conditional irrelevance (or independence) can be verified in time linear in the size of the graph. To resolve a particular inference query or decision problem, only some of the possible states and probability distributions must be specified, the "requisite information." This paper presents a new, simple, and efficient "Bayes-ball" algorithm which is wellsuited to both new students of belief networks and state of the art implementations. The Bayes-ball algorithm determines irrelevant sets and requisite information more efficiently than existing methods, and is linear in the size of the graph for belief networks and influence diagrams.