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Continuous Time Bayesian Networks with Clocks

ICML 2020 · Probabilistic and scientific modeling › Molecules and proteins · approach: Neural point-process models · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use node-wise clocks with Weibull survival times instead of exponential survival timesCTBN structure inference · Synthetic Weibull trajectoriesAUROCHelped
Use node-wise clocks with Gamma survival times instead of exponential survival timesCTBN structure inference · Synthetic Gamma trajectoriesAUROCHelped
Use node-wise clocks with Gamma survival times instead of exponential survival timesCTBN structure inference · GeneNetWeaver E. coli gene regulatory networkedge prediction correctnessHelped
Increase the Weibull shape parameter away from the exponential caseTraditional CTBN structure inference · Synthetic Weibull trajectories with fixed shapeAUROCHurt
Use a Weibull CTBN rather than a traditional CTBN on exponential dataCTBN structure inference · Synthetic Weibull trajectories with shape k=1AUROCNo clear effect
Fit Weibull or Gamma survival-time parameters from more observed transitionsAugmented CTBN parameter inference · Synthetic trajectoriesMSEHelped

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