bayesian-model

This paper proposes the Bayesian Expected Uncertainty Reduction (B-EUR) model to formalize how design options are valued based on their expected reduction of epistemic uncertainty, and validates it through simulations and human experiments in a graph-shape guessing task.

This paper studies performance prediction for symbolic (e.g., Python) and prompt programs using a Bayesian coin-flip model, finding that symbolic programs have all-or-nothing performance while prompt programs have diffuse priors, and introduces RAP (Retrieved Approximate Prior) for performance prediction.