Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Readings in uncertain reasoning
Elements of information theory
Elements of information theory
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Probabilistic Reasoning in Multi-Agent Systems: A Graphical Models Approach
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Robust probabilistic inference in distributed systems
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Learning Bayesian Networks
Efficient Distributed Bayesian Reasoning via Targeted Instantiation of Variables
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
A multi-agent systems approach to distributed bayesian information fusion
Information Fusion
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Network fragments: representing knowledge for constructing probabilistic models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
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This paper discusses an approach to distributed Bayesian modeling and inference, which is relevant for an important class of contemporary real world situation assessment applications. By explicitly considering the locality of causal relations, the presented approach (i) supports coherent distributed inference based on large amounts of very heterogeneous information, (ii) supports a systematic validation of distributed models and (iii) can be robust with respect to the modeling deviations of parameters. The challenges of distributed situation assessment applications and their solutions are explained with the help of a real world example from the gas monitoring domain.