arXiv (All)
open access
Convergence Analysis of a Greedy Algorithm for Conditioning Gaussian Random Variables
In the context of Gaussian conditioning, greedy algorithms iteratively select the most informative measurements, given an observed Gaussian random variable. However, the convergence analysis for conditioning Gaussian random variables remains an open problem. We adress this by introducing an operator
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
- The AECM Algorithm for Deterministic Maximum Likelihood Direction Finding in the Presence of Gaussian Mixture Noise#1013558
- The Gaussian Latent Machine: Efficient Prior and Posterior Sampling for Inverse Problems#144295
- Exact Bayes Regret and Asymptotic Optimality in High-Dimensional Gaussian Bandits#1076374
- General bounds on Bayes errors for regression with Gaussian processes#230073
- Free-Probabilistic State Evolution and Random Matrix Discrepancy#999518
Record · ID 1014492
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