ML Anthology

An Event-Based Framework for Process Inference

Michael Joya
AAAI 2011 pp. 1796-1797
doi:10.1609/AAAI.V25I1.8064 /aaai/2011/joya2011aaai-event/

Abstract

We focus on a class of models used for representing the dynamics between a discrete set of probabilistic events in a continuous-time setting. The proposed framework offers tractable learning and inference procedures and provides compact state representations for processes which exhibit variable delays between events. The approach is applied to a heart sound labeling task that exhibits long-range dependencies on previous events, and in which explicit modeling of the rhythm timings is justifiable by cardiological principles.

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Text

Joya. "An Event-Based Framework for Process Inference." AAAI Conference on Artificial Intelligence, 2011. doi:10.1609/AAAI.V25I1.8064

Markdown

[Joya. "An Event-Based Framework for Process Inference." AAAI Conference on Artificial Intelligence, 2011.](https://mlanthology.org/aaai/2011/joya2011aaai-event/) doi:10.1609/AAAI.V25I1.8064

BibTeX

@inproceedings{joya2011aaai-event,
  title     = {{An Event-Based Framework for Process Inference}},
  author    = {Joya, Michael},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2011},
  pages     = {1796-1797},
  doi       = {10.1609/AAAI.V25I1.8064},
  url       = {https://mlanthology.org/aaai/2011/joya2011aaai-event/}
}