Bibliographic record
Abstract
Given a discrete time finite state hidden Markov model (HMM) and a sequence of observations, there are different ways to estimate the hidden behavior of the system. In this paper, the problem of finding the most probable state sequence is considered. The state sequence, as opposed to the state trajectory, specifies the sequence of states that the HMM visits but does not specify the dwelling times in these states. This inference problem is relevant in a variety of domains, like text analysis, speech recognition, or behavior recognition, where the exact timing of hidden state transitions is not nearly as important as the sequence of states visited. No existing algorithm addresses this inference question adequately. Leveraging previous work on continuous time Markov chains, we develop a provably correct algorithm, called state sequence analysis, that addresses this inference question in HMMs. We discuss and illustrate empirically the differences between finding the most probable state sequence directly and doing so through running the Viterbi algorithm and collapsing repetitive state visitations. Experimental results in two synthetic domains demonstrate that the Viterbi-based approach can be significantly suboptimal compared to state sequence analysis. Further, we demonstrate the benefits of the proposed approach on a real activity recognition problem.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".