Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Formulation of tradeoffs in planning under uncertainty
Formulation of tradeoffs in planning under uncertainty
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
The independent choice logic for modelling multiple agents under uncertainty
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Structured probabilistic models: Bayesian networks and beyond
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A machine program for theorem-proving
Communications of the ACM
Decomposable negation normal form
Journal of the ACM (JACM)
Probabilistic Logic Programming and Bayesian Networks
ACSC '95 Proceedings of the 1995 Asian Computing Science Conference on Algorithms, Concurrency and Knowledge
Dynamic construction of belief networks
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
A differential approach to inference in Bayesian networks
Journal of the ACM (JACM)
Probabilistic reasoning for complex systems
Probabilistic reasoning for complex systems
Reasoning about Uncertainty
First-order probabilistic inference
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Approximate inference for first-order probabilistic languages
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Local learning in probabilistic networks with hidden variables
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Goal oriented symbolic propagation in Bayesian networks
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Sensitivity analysis in discrete Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Turing's dream and the knowledge challenge
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
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We characterize probabilities in Bayesian networks in terms of algebraic expressions called quasi-probabilities. These are arrived at by casting Bayesian networks as noisy AND-OR-NOT networks, and viewing the subnetworks that lead to a node as arguments for or against a node. Quasi-probabilities are in a sense the "natural" algebra of Bayesian networks: we can easily compute the marginal quasi-probability of any node recursively, in a compact form; and we can obtain the joint quasi-probability of any set of nodes by multiplying their marginals (using an idempotent product operator). Quasi-probabilities are easily manipulated to improve the efficiency of probabilistic inference. They also turn out to be representable as square-wave pulse trains, and joint and marginal distributions can be computed by multiplication and complementation of pulse trains.