Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A model for reasoning about persistence and causation
Computational Intelligence
A computational scheme for reasoning in dynamic probabilistic networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Automatic symbolic traffic scene analysis using belief networks
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Temporal verification of reactive systems: safety
Temporal verification of reactive systems: safety
The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Stochastic simulation algorithms for dynamic probabilistic networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Object-oriented Bayesian networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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
Exploiting the architecture of dynamic systems
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Influence-based model decomposition
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Influence-based model decomposition for reasoning about spatially distributed physical systems
Artificial Intelligence
CL '00 Proceedings of the First International Conference on Computational Logic
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Force deployment analysis with generalized grammar
Information Fusion
Dynamic probabilistic relational models
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Real-time inference with large-scale temporal bayes nets
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Sufficiency, separability and temporal probabilistic models
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Learning models of relational stochastic processes
ECML'05 Proceedings of the 16th European conference on Machine Learning
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This paper considers the problem of representing complex systems that evolve stochastically over time. Dynamic Bayesian networks provide a compact representation for stochastic processes. Unfortunately, they are often unwieldy since they cannot explicitly model the complex organizational structure of many real life systems: the fact that processes are typically composed of several interacting subprocesses, each of which can, in tum, be further decomposed. We propose a hierarchically structured representation language which extends both dynamic Bayesian networks and the object-oriented Bayesian network framework of [9], and show that our language allows us to describe such systems in a natural and modular way. Our language supports a natural representation for certain system characteristics that are hard to capture using more traditional frameworks. For example, it allows us to represent systems where some processes evolve at a different rate than others, or systems where the processes interact only intermittently. We provide a simple inference mechanism for our representation via translation to Bayesian networks, and suggest ways in which the inference algorithm can exploit the additional structure encoded in our representation.