arXiv (All)
open access
Learning Global Sensitivity Indices from Observational Data: A Metamodel-Based Approach
Classical variance-based Global Sensitivity Analysis (GSA) assumes that the input--output mechanism can be repeatedly evaluated under a designed sampling scheme, which is infeasible when only a given sample of observations is available. We propose MM--GSA, a metamodel-based approach to GSA from obse
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
- Measuring the Sensitivity of Classification Models with the Error Sensitivity Profile#141481
- Distributional Sensitivity Analysis: Enabling Differentiability in Sample-Based Inference#665521
- Distributional Sensitivity Analysis: Enabling Differentiability in Sample-Based Inference#664021
- Global Sensitivity Analysis of Spatial Sets: Finite-Element-Based Estimation and the Role of Observation Windows#1000144
- Tractable Uncertainty-Aware Meta-Learning#128257
Record · ID 1154601
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