Bibliographic record
Abstract
We consider the problem of optimal decentralized estimation of a linear stochastic process by multiple agents. Each agent receives a noisy observation of the state of the process and delayed observations of its neighbors (according to a pre-specified, strongly connected, communication graph). Based on their observations, all agents generate a sequence of estimates of the state of the process. The objective is to minimize the total expected weighted mean square error between the state and the agents' estimates over a finite horizon. In centralized estimation with weighted mean square error criteria, the optimal estimator does not depend on the weight matrix in the cost function. We show that this is not the case when the information is decentralized. The optimal decentralized estimates depend on the weight matrix in the cost function. In particular, we show that the optimal estimate consists of two parts: a common estimate which is the conditional mean of the state given the common information and a correction term which is a linear function of the offset of the local information from the conditional expectation of the local information given the common information. The corresponding gain depends on the weight matrix as well as on the covariance between the offset of agents' local information from the conditional expectation of the local information given the common information. We show that the common estimate can be computed from single Kalman filter and derive recursive expressions for computing the offset covariances and the estimation gains.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".