An Event-Based Framework for Process Inference

Authors

  • Michael Joya Department of Computing Science University of Alberta

DOI:

https://doi.org/10.1609/aaai.v25i1.8064

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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Published

2011-08-04

How to Cite

Joya, M. (2011). An Event-Based Framework for Process Inference. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 1796-1797. https://doi.org/10.1609/aaai.v25i1.8064