On Undirected Representations of Bayesian Networks
Why this work is in the frame
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Bibliographic record
Abstract
Empirical studies clearly demonstrate the effectiveness of the nested jointree (NJT) representation in probabilistic inference. A NJT is a traditional Markov network (MN) together with a possible local MN nested in each clique. These nested MNs can themselves contain other nested MNs in a recursivemanner. However, the NJT representation is not necessarily a faithful representation of a given Bayesian network (BN). This means that the effectiveness of a NJT has been demonstrated while only exploiting some of the independency information available in the given BN. In this paper, weintroduce a new kind of probabilistic network, called a hierarchical Markov network (HMN). A HMN is a hierarchy of MNs. We give an algorithm to transform a BN into a canonical HMN. The main result of this paper is that the constructed HMN is unique and equivalent to the input BN. Since HMNs are a faithful representation of BNs, a query may be optimized using independencies in a HMN that otherwise would have gone unrepresentedinaNJTapproach. 1
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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 | 0.000 | 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 it