NCBI PubMed Central
open access
Scaling Gaussian Processes for Deterministic Model Predictive Control with Online Updates: A Framework for Model Selection and Re-identification in Process Control.
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
- Scalable Gaussian Process Regression via Deterministic Trigonometric Features: Uniform Bounds for Safe Model Predictive Control#620808
- Scalable Gaussian Process Regression via Deterministic Trigonometric Features: Uniform Bounds for Safe Model Predictive Control#619129
- On Basis Function Selection for Sparse Gaussian Process Regression#1043091
- On Basis Function Selection for Sparse Gaussian Process Regression#1064018
- Evaluating covariate balance for long time horizon Markov decision processes#1070879
Record · ID 613368
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