Bibliographic record
Abstract
The best-performing on-line methods for estimating probabilities of symbols in a sequence (required for computing minimal codes) use context trees with either information-theoretic state selection or context-tree weighting. This paper derives de novo from Bayes' theorem, a novel technique for modeling sequences on-line with context trees, which we call "Bayesian state combining" or BSC. BSC is comparable in function to both information-theoretic state selection and context-tree weighting. However, it is a truly distinct alternative to either of these techniques, which like BSC, can be viewed as "dispatchers" of probability estimates from the set of competing, memoryless models represented by the context tree. The resulting technique handles sequences over m-ary input alphabets for arbitrary m and may employ any probability estimator applicable to context models (e.g., Laplace, Krichevsky-Trofimov, blending, and more generally, mixtures). In experiments that control other (256-ary) context-tree model features such as Markov order and probability estimators, we compare the performance of BSC and information-theoretic state selection. The background notation and concepts are reviewed, as required to understand the modeling problem and application of our result. The leading notion of the paper is derived, which dynamically maps certain states in context-models to a set of mutually exclusive hypotheses and their prior and posterior probabilities. The efficient sequential computation of the posterior probabilities of the hypotheses, which was made possible via a non-obvious application of the percolating description-length update mechanism introduced by Bunton (see Proceedings Data Compression Conference, IEEE Computer Society Press, 1997) is described. The preliminary empirical performance of the technique on the Calgary Corpus is presented, the relationship of BSC to information-theoretic state selection and context-tree weighting is discussed.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".