A knowledge representation scheme for the bayesian network model
Bibliographic record
Abstract
The Bayesian network model forms the basis for probabilistic reasoning in this exposition. A Bayesian network consists of a directed acyclic graph, and a set of conditional probability tables. The directed acyclic graph encodes the conditional independencies of the domain variables. The product of the conditional probability tables defines a joint probability distribution representing the domain knowledge. In order to build a Bayesian network, one may construct the directed acyclic graph based on the causal relationships of the variables involved. However, in many applications it may be necessary to construct the directed acyclic graph from the conditional independency information supplied by the experts. This thesis suggests a hierarchical characterization of input conditional independencies that can be faithfully represented by a Bayesian network. This characterization leads to an alternative representation of Bayesian networks. Methods have been developed to construct suitable hierarchical covers for an input set of conditional independencies. These covers can be represented by an alternative graphical structure defined by a hierarchical set of acyclic hypergraphs. Probabilistic inference can be directly performed using these hypergraphs without the need to first convert them into a secondary structure as in conventional Bayesian networks. Moreover, a method is suggested to compute the marginals of the proposed representation such that all the input independency information is preserved for probabilistic reasoning.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".