Research map / Probabilistic and scientific modeling / Variational and generative sampling
A Unifying Variational Inference Framework for Hierarchical Graph-Coupled HMM with an Application to Influenza Infection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace Gibbs EM inference with sigmoid variational inference | hGCHMM · exFlu real flu data | recall: 0.8974 -> 0.9548 | Helped |
| replace Gibbs EM inference with sigmoid variational inference | hGCHMM · exFlu real flu data | accuracy: 0.9978 -> 0.9946 | No clear effect |
| use sigmoid VI instead of Gibbs EM at high infection rates (severe flu) | hGCHMM · semi-synthetic MIT data | prediction accuracy / ROC | Helped |
| use sigmoid VI instead of Gibbs EM at low infection rates (usual flu) | hGCHMM · semi-synthetic MIT data | prediction accuracy / ROC | No clear effect |
| add a deeper hidden layer to the sigmoid recognition model (ablation) | SigVI · hGCHMM inference | performance | No clear effect |
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