Revising Markov boundary for multiagent probabilistic inference
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Multiply sectioned Bayesian networks (MSBNs) extend Bayesian networks (BNs) to graphical models that provide a coherent framework for probabilistic inference in cooperative multiagent distributed interpretation systems. Observation plays an important role in the inference with graphical models. Since observation of each observable variable has a cost, it would be helpful if we can find the most relevant variables to observe. In a probabilistic model, a Markov boundary of a variable provides a minimal set of variables that shields the variable from the influence of all other variables. However, the concept cannot be used directly for observation. First, it is generally intractable to verify conditional independencies in a probabilistic model. Second, the Markov boundary members may not be observable. Third, it is defined only for a single variable. Finally, it is not unique. By revising the concept to address these issues, we introduce the concept of observable Markov boundary of a set of nodes defined on d-separation of graphical models. The observable Markov boundary captures all relevant variables to observe for probabilistic inference with graphical models. In an MSBN, the observable Markov boundary of a set of nodes may span across all Bayesian subnets. We present an algorithm for cooperative computation of the observable Markov boundary of a set of nodes in an MSBN without revealing subnet structures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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