Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Structured representation of complex stochastic systems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Query DAGs: a practical paradigm for implementing belief-network inference
Journal of Artificial Intelligence Research
Symbolic probabilistic inference in belief networks
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Multiplicative factorization of noisy-max
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Modeling time-varying uncertain situations using Dynamic Influence Nets
International Journal of Approximate Reasoning
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An increasing number of applications require real-time reasoning under uncertainty with streaming input. The temporal (dynamic) Bayes net formalism provides a powerful representational framework for such applications. However, existing exact inference algorithms for dynamic Bayes nets do not scale to the size of models required for real world applications which often contain hundreds or even thousands of variables for each time slice. In addition, existing algorithms were not developed with real-time processing in mind. We have developed a new computational approach to support real-time exact inference in large temporal Bayes nets. Our approach tackles scalability by recognizing that the complexity of the inference depends on the number of interface nodes between time slices and by exploiting the distinction between static and dynamic nodes in order to reduce the number of interface nodes and to factorize their joint probability distribution. We approach the real-time issue by organizing temporal Bayes nets into static representations, and then using the symbolic probabilistic inference algorithm to derive analytic expressions for the static representations. The parts of these expressions that do not change at each time step are pre-computed. The remaining parts are compiled into efficient procedural code so that the memory and CPU resources required by the inference are small and fixed.