# Reasoning at the Right Time Granularity

@inproceedings{Saria2007ReasoningAT, title={Reasoning at the Right Time Granularity}, author={S. Saria and U. Nodelman and D. Koller}, booktitle={UAI}, year={2007} }

Most real-world dynamic systems are composed of different components that often evolve at very different rates. In traditional temporal graphical models, such as dynamic Bayesian networks, time is modeled at a fixed granularity, generally selected based on the rate at which the fastest component evolves. Inference must then be performed at this fastest granularity, potentially at significant computational cost. Continuous Time Bayesian Networks (CTBNs) avoid time-slicing in the representationâ€¦Â Expand

#### 49 Citations

Importance Sampling for Continuous Time Bayesian Networks

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Uncertain and negative evidence in continuous time Bayesian networks

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