AAAI Publications, Twenty-Fifth AAAI Conference on Artificial Intelligence

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An Event-Based Framework for Process Inference
Michael Joya

Last modified: 2011-08-04


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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