Graphical model inference with external network data
Bibliographic record
Abstract
A frequent challenge when using graphical models in practice is that the sample size is limited relative to the number of parameters. They also become hard to interpret when the number of variables p gets large. We consider applications where one has external data, in the form of networks between variables, that can improve inference and help interpret the fitted model. An example of interest regards the interplay between social media and the co-evolution of the COVID-19 pandemic across USA counties. We develop a spike-and-slab prior framework that depicts how partial correlations depend on the networks, by regressing the edge probabilities, average partial correlations, and their variance on the networks. The goal is to detect when the network data relates to the graphical model and, if so, explain how. We develop computational schemes and software in R and probabilistic programming languages. Our applications show that incorporating network data can improve interpretation, statistical accuracy, and out-of-sample prediction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".