Probabilistic reasoning with hierarchically structured variables
Bibliographic record
Abstract
Many practical problems have random variables with a large number of values that can be hierarchically structured into an abstraction tree of classes. This paper considers how to represent and exploit hierarchical structure in probabilistic reasoning. We represent the distribution for such variables by specifying, for each class, the probability distribution over its immediate subclasses. We represent the conditional probability distribution of any variable conditioned on hierarchical variables using inheritance. We present an approach for reasoning in Bayesian networks with hierarchically structured variables that dynamically constructs a flat Bayesian network, given some evidence and a query, by collapsing the hierarchies to include only those values necessary to answer the query. This can be done with a single pass over the network. We can answer the query from the flat Bayesian network using any standard probabilistic inference algorithm such as variable elimination or stochastic simulation. The domain size of the variables in the flat Bayesian network is independent of the size of the hierarchies; it depends on how many of the classes in the hierarchies are directly associated with the evidence and query. Thus, the representation is applicable even when the hierarchy is conceptually infinite. 1
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".