Bibliographic record
Abstract
Graphical models such as Bayesian networks (BNs) (Pearl, 1988; Jensen & Nielsen, 2007) and decomposable Markov networks (DMNs) (Xiang, Wong., & Cercone, 1997) have been widely applied to probabilistic reasoning in intelligent systems. Knowledge representation using such models for a simple problem domain is illustrated in Figure 1: Virus can damage computer files and so can a power glitch. Power glitch also causes a VCR to reset. Links and lack of them convey dependency and independency relations among these variables and the strength of each link is quantified by a probability distribution. The networks are useful for inferring whether the computer has virus after checking files and VCR. This chapter considers how to discover them from data. Discovery of graphical models (Neapolitan, 2004) by testing all alternatives is intractable. Hence, heuristic search are commonly applied (Cooper & Herskovits, 1992; Spirtes, Glymour, & Scheines, 1993; Lam & Bacchus, 1994; Heckerman, Geiger, & Chickering, 1995; Friedman, Geiger, & Goldszmidt, 1997; Xiang, Wong, & Cercone, 1997). All heuristics make simplifying assumptions about the unknown data-generating models. These assumptions preclude certain models to gain efficiency. Often assumptions and models they exclude are not explicitly stated. Users of such heuristics may suffer from such exclusion without even knowing. This chapter examines assumptions underlying common heuristics and their consequences to graphical model discovery. A decision theoretic strategy for choosing heuristics is introduced that can take into account a full range of consequences (including efficiency in discovery, efficiency in inference using the discovered model, and cost of inference with an incorrectly discovered model) and resolve the above issue.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".