Distributed Parameter Estimation with Side Information: A Factor Graph Approach
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Bibliographic record
Abstract
In this paper, a low complexity algorithm for distributed maximum likelihood estimation of a binary symmetric source (BSS) using side-information is proposed. The estimation is formulated as an incomplete-data problem and is solved by the expectation-maximization (EM) algorithm. A low-complexity implementation of the algorithm using coset codes and LDPC-based syndrome decoding with message passing over factor-graph is also proposed. The algorithm is a generalization of the LDPC-based syndrome decoding algorithm for the case when the probability distribution of the source is not known a-priori. Hence, the algorithm may be considered as a tool for achieving the corner points of the Slepian-Wolf (SW) region in distributed coding when the correlation channel information is not available. The estimation efficiency is studied by comparing the mean square error with the achievable Fisher information.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it