Bibliographic record
Abstract
This volume is a collection of papers on hidden Markov processes (HMPs) involving connections with symbolic dynamics and statistical mechanics. The subject was the focus of a five-day workshop held at the Banff International Research Station (BIRS) in October 2007, which brought together thirty mathematicians, computer scientists, and electrical engineers from institutions throughout the world. Most of the papers in this volume are based either on work presented at the workshop or on problems posed at the workshop. From one point of view, an HMP is a stochastic process obtained as the noisy observation process of a finite-state Markov chain; a simple example is a binary Markov chain observed in binary symmetric noise, i.e., each symbol (0 or 1) in a binary state sequence generated by a two-state Markov chain may be flipped with some small probability, independently from time instant to time instant. In another (essentially equivalent) viewpoint, an HMP is a process obtained from a finite-state Markov chain by partitioning its state set into groups and completely “hiding” the distinction among states within each group; more precisely, there is a deterministic function on the states of the Markov chain, and the HMP is the process obtained by observing the sequences of function values rather than sequences of states (and hence such a process is sometimes called a “function of a Markov chain”). HMPs are encountered in an enormous variety of applications involving phenomena observed in the presence of noise.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.354 | 0.228 |
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".