Learnability of Timescale Graphical Event Models

Philipp Behrendt

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arXiv · 2005.12186

Learnability of Timescale Graphical Event Models

Philipp Behrendt

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Abstract

This technical report tries to fill a gap in current literature on Timescale Graphical Event Models. I propose and evaluate different heuristics to determine hyper-parameters during the structure learning algorithm and refine an existing distance measure. A comprehensive benchmark on synthetic data will be conducted allowing conclusions about the applicability of the different heuristics.

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Philipp Behrendt. 2020-05-25. Learnability of Timescale Graphical Event Models. https://arxiv.org/abs/2005.12186

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