arXiv (All)
open access
Risk-Aware Goal-Oriented Bayesian Optimal Experimental Design
Traditional Bayesian optimal experimental design (OED) selects measurements that best inform a model's parameters. However, such measurements can be suboptimal for downstream predictions. Goal-oriented OED targets the prediction directly. However, the existing goal-oriented criteria value all reduct
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Prediction-Oriented Sequential Bayesian Design of Physical Experiments Informed by Computer Models#1141504
- Goal-driven Bayesian Optimal Experimental Design for Robust Decision-Making Under Model Uncertainty#224497
- Bayesian optimal designs for common single-driver experiments in ecology#319765
- Energy-aware frugal Bayesian optimization#1115594
- GoalEvolve: From Handcrafted Algorithm Priors to Goal-Driven Evolution of Physical Design Algorithms#625292
Record · ID 985423
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive