Research map / Probabilistic and scientific modeling / Variational and generative sampling
Variational Inference for sparse network reconstruction from count data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Account for sampling effort with sample-specific offsets | PLNnetwork versus graphical-Lasso and sparse LLM · Simulated compositional counts | AUC | Helped |
| Account for sampling effort with sample-specific offsets | PLNnetwork versus graphical-Lasso and sparse LLM · Simulated compositional counts | AUPR | Helped |
| Model covariate effects jointly with the network | PLNnetwork · Scale-free, random, and community simulations | AUPR | Helped |
| Model covariate effects jointly with the network | PLNnetwork · Community networks with small covariate effect and n=p/2 | AUC: .62 versus .62 | No clear effect |
| Use StARS rather than BIC for selecting the penalty | PLNnetwork · Simulated 50-node random networks | Precision and recall | Helped |
| Use StARS rather than BIC for selecting the penalty | PLNnetwork · Simulated 50-node random networks | Computational cost | Hurt |
| Add temperature and depth covariates to the network model | PLNnetwork · Barents fish | Number of inferred edges | Hurt |
| Add a sampling-effort offset | PLNnetwork · French presidential election 2017 | Network density | Helped |
| Add department as a geography covariate | PLNnetwork · French presidential election 2017 | Latent-space fit to Gaussian ellipticity | Helped |
| Jointly estimate covariate effects and network structure | PLNnetwork · Oak mildew | Cross-group network coverage | Helped |
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