Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Fusion and propagation with multiple observations in belief networks
Artificial Intelligence
Qualitative probabilities: a normative framework for commonsense reasoning
Qualitative probabilities: a normative framework for commonsense reasoning
Generalized Queries on Probabilistic Context-Free Grammars
IEEE Transactions on Pattern Analysis and Machine Intelligence
WebMath: A Web-Based ITS System
ICWL '02 Proceedings of the First International Conference on Advances in Web-Based Learning
Electronic Homework on the WWW
WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
The state of play in machine/environment interactions
Artificial Intelligence Review
Improvements to message computation in lazy propagation
International Journal of Approximate Reasoning
Real-time inference with large-scale temporal bayes nets
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
A differential approach to inference in Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
A standard approach for optimizing belief network inference using query DAGs
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Loopy belief propagation as a basis for communication in sensor networks
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
A search problem in complex diagnostic Bayesian networks
Knowledge-Based Systems
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We describe a new paradigm for implementing inference in belief networks, which consists of two steps: (1) compiling a belief network into an arithmetic expression called a Query DAG (Q-DAG); and (2) answering queries using a simple evaluation algorithm. Each node of a Q-DAG represents a numeric operation, a number, or a symbol for evidence. Each leaf node of a Q-DAG represents the answer to a network query, that is, the probability of some event of interest. It appears that Q-DAGs can be generated using any of the standard algorithms for exact inference in belief networks -- we show how they can be generated using clustering and conditioning algorithms. The time and space complexity of a Q-DAG generation algorithm is no worse than the time complexity of the inference algorithm on which it is based. The complexity of a Q-DAG evaluation algorithm is linear in the size of the Q-DAG, and such inference amounts to a standard evaluation of the arithmetic expression it represents. The intended value of Q-DAGs is in reducing the software and hardware resources required to utilize belief networks in on-line, real-world applications. The proposed framework also facilitates the development of on-line inference on different software and hardware platforms due to the simplicity of the Q-DAG evaluation algorithm. Interestingly enough, Q-DAGs were found to serve other purposes: simple techniques for reducing Q-DAGs tend to subsume relatively complex optimization techniques for belief-network inference, such as network-pruning and computation-caching.