Stochastic block–monotonicity in the approximation of the stationary distribution of infinite markov chains
Bibliographic record
Abstract
Markov chains with block-structured transition matrices find many applications in various areas. Such Markov chains are characterized by having a state space which is partitioned into levels, each level consisting of a number of stages. Examples include Markov chains of GI/M/1 type and M/G/l type, and, more generally, Markov chains of Toeplitz type. The level-dependent quasi-birth-and-death (LDQBD) process provides an additional example; the transition matrix does not have repeating blocks in this case. In the analysis of such Markov chains, a number of properties and/or measures which relate to transitions among levels play a dominant role, while transitions between stages within the same level are less important In this paper, we introduce the concept of block-monotonicity and apply this notion to the analysis of Markov chains possessing block structure. In particular, the problem of approximating the stationary probability vectors is successfully treated
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".