Bayesian Inference Using Gibbs Sampling in Applications and Curricula of Decision Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Applications and curricula of decision analysis currently do not include methods to compute Bayes' rule and obtain posteriors for nonconjugate prior distributions. The current convention is to force the decision maker's belief to take the form of a conjugate distribution, leading to a suboptimal decision. Bayesian inference using Gibbs sampling (BUGS) software, which uses Markov chain Monte Carlo methods, numerically obtains posteriors for nonconjugate priors. By using the decision maker's true nonconjugate belief, the problems explored suggest that BUGS can produce a posterior distribution that leads to optimal decision making. Other methods exist that can use nonconjugate priors, but they must be implemented ad hoc because they do not have any supporting software. BUGS offers the distinct advantage of being implemented in existing software, and with simple coding can solve a wide range of decision analysis problems. BUGS is useful in making optimal decisions, and it is easy to learn and implement; therefore, including BUGS in decision analysis curricula is valuable.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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